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Journal articles on the topic 'Biometrics'
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1
Yaacob, Mohd Noorulfakhri, Syed Zulkarnain Syed Idrus, Wan Azani Wan Mustafa, Mohd Aminudin Jamlos, and Mohd Helmy Abd Wahab. "Identification of the Exclusivity of Individual’s Typing Style Using Soft Biometric Elements." Annals of Emerging Technologies in Computing 5, no. 5 (March 20, 2021): 10–26. http://dx.doi.org/10.33166/aetic.2021.05.002.
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Biometric is used as a main security fence in a computer system. The unique characteristics of a person can be distinguished from each other. Human’s biometrics can be categorized into three types: morphological, biological and behavioural. Morphological biometrics uses physical features for recognition. Biological biometrics used to identify user based on biological features. Behavioural biometrics such as gender, culture, height and weight can be used as an additional security measure within a system. These biometric behavioural features are also known as soft biometric. This study uses soft biometric elements (gender, culture, region of birth and educational level) in the keystroke dynamic study to distinguish typing patterns in each of these categories. The Support Vector Machine (SVM) classification method is used to perform this classification for soft biometric identification. The results of this study have shown that soft biometrics in keystroke dynamic can be used to distinguish group of individuals typing.
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B.R., Rohini, and Thippeswamy G. "BIOMETRICS-A PRELIMINARY APPROACH." International Journal of Research -GRANTHAALAYAH 5, no. 4RACSIT (April 30, 2017): 47–52. http://dx.doi.org/10.29121/granthaalayah.v5.i4racsit.2017.3350.
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Authentication plays a vital role in Information security. The need for identification of legitimate user has increased in the waking concerns for global security. Biometric recognition Systems is a major tool for Authentication mechanism. Biometrics is the ability to identify and authenticate an individual using one or more of their behavioral or physical characteristics. The Study of Different Biometric Modalities gives a better understanding of Biometric Techniques. We focus our Study on Face Biometrics. This paper emphasizes on better understanding of introduction to Biometrics, Biometric Modalities and Face recognition Techniques.
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Michelsen, Lea Laura. "Thinking Beyond Biometrics." A Peer-Reviewed Journal About 7, no. 1 (July 6, 2018): 36–49. http://dx.doi.org/10.7146/aprja.v7i1.115063.
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Today, digital biometrics are proliferating. Based on scans of biological traits – from faces, fingerprints and gait to vein patterns, heart rhythm, brain activity, and body odor – biometrics are known to be able to establish the identity of a human subject. When reading humanities research on biometrics, though, it becomes evident that we are altering a lot more than just our faces. This article proposes a study of a wave of artistic counter-biometrics in order to enable thinking beyond the biometric box, practicing the ‘art of disappearing’ from the biometric gaze. With an outset in Zach Blas’ Face Cages (2013- 16) and his “Fag Face” mask from Facial Weaponization Suite (2011-14) the article argues that biometrics produces an aesthetics, and that it should be treated as such. This shifts our perspective from the technical media to the narratives we inscribe in these media and the aesthetic output enabled by that. Activating a counter- biometric aesthetics is far from naïve. On the contrary, engaging in the aesthetics of biometrics is a valuable and urgently needed research strategy for dealing with the physiognomic renaissance biometrics brings about.
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Mitianov, Z. O. "Compulsory and Voluntary Biometrics: Content and Distinction in Russian Law." Theoretical and Applied Law, no. 3 (October 8, 2024): 63–74. http://dx.doi.org/10.22394/3034-2813-2024-3-63-74.
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The article examines the peculiarities of separation and differentiation of two areas included in the sphere of biometric personal data processing in Russia: compulsory and voluntary biometrics. The definitions of the concepts of these areas are formulated, the peculiarities of their content are considered. Justification of their differentiation, main criteria and specifics of such differentiation are given. The problems of voluntary biometrics in the context of exceptional cases of compulsory processing of biometric personal data are highlighted. The balance of private and public interests in each of the areas is studied. The conclusions are drawn about the formation of the scope of voluntary biometrics and its differentiation from compulsory biometrics due to the legislative novelties at the end of 2022; the essential difference between compulsory biometrics and voluntary biometrics; the existence of special criteria for differentiating between voluntary and compulsory biometrics; the need to focus lawmaking efforts on voluntary biometrics due to its dynamism and closeness to everyday life.
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Yang, Wencheng, Song Wang, Hui Cui, Zhaohui Tang, and Yan Li. "A Review of Homomorphic Encryption for Privacy-Preserving Biometrics." Sensors 23, no. 7 (March 29, 2023): 3566. http://dx.doi.org/10.3390/s23073566.
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The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.
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., Himanshi, Trisha Gulati, and Yasha Hasija. "Biometrics in Healthcare." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 3, no. 2 (April 15, 2018): 13–17. http://dx.doi.org/10.35121/ijapie201804223.
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Biometrics is the discipline to measure physical human characteristics for the identification and authentication of an individual. Since ancient times, people have used voice, face, and other characteristics for the identification of an individual. With evolution, we take the individual characteristics like fingerprint scans, retina and iris images, etc., as inputs to the computer systems and then store or verify them with existing records. This report discusses biometrics and its recent roles found in the field of healthcare, medicine, genetics, and biotechnology. It includes the concept of biometrics, the system used for biometric recognition and its working, types of biometric systems, the different system algorithms applied, and system modules which are well illustrated with flow charts and block diagrams. Some of the health institutes in developed countries have started using biometric systems for checking patients and/or doctors. Biometry has enabled the proper organization and storage of the health records of individuals in medical institutes. Biometric authentication is also finding a distinct role in foiling medical claims fraud highlighting the advantages it. Even after processing via a very accurate biometric system, there is a chance of a false result due to some disease or injury to the body part subjected to biometry or faulty system leading to some error. There is also a possibility that the biometric system may harm our bodies. Moreover, biometric records need really tight system security to prevent any kind of misuse. Biometrics has a great potential to find a lot more uses in the field of healthcare. Many ideas are being proposed for implementation. In the future, biometrics can be used to detect potential disease and risks by using methods like adiposity measurement and Gas Discharge Visualization (GDV).
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Sharma, Tushar, and Upinder Kaur. "Biometric Security: A Review to Future." Revista Gestão Inovação e Tecnologias 11, no. 4 (July 29, 2021): 3758–68. http://dx.doi.org/10.47059/revistageintec.v11i4.2405.
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This paper presents the different biometric with their limitations and introduces their alternative in form of brain biometric, Breath biometrics, and Tongue biometrics. Brain biometric uses brain wave while breath biometric uses one’s breath and tongue biometric uses a tongue’s shape and variation to distinguish them and present a good alternative for the presently used biometric like fingerprint, iris recognition, face recognition.
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Shopon, Md, Sanjida Nasreen Tumpa, Yajurv Bhatia, K. N. Pavan Kumar, and Marina L. Gavrilova. "Biometric Systems De-Identification: Current Advancements and Future Directions." Journal of Cybersecurity and Privacy 1, no. 3 (August 31, 2021): 470–95. http://dx.doi.org/10.3390/jcp1030024.
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Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.
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Choudhury, Bismita, Patrick Then, Biju Issac, Valliappan Raman, and Manas Kumar Haldar. "A Survey on Biometrics and Cancelable Biometrics Systems." International Journal of Image and Graphics 18, no. 01 (January 2018): 1850006. http://dx.doi.org/10.1142/s0219467818500067.
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Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.
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Sable, Harsh, and Divya Bajpai Tripathy. "A Review on Comparative Analysis on Different Sort of Physiological and Behavioral Biometric Framework." International Journal of Advance Research and Innovation 9, no. 2 (2021): 1–9. http://dx.doi.org/10.51976/ijari.922101.
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Biometrics as the investigation of seeing an individual ward on their physical or conduct characteristics, biometric have now been conveyed in diverse business, ordinary resident and national security applications. Customarily the usage of biometrics devices has improved our capacity to give approved entry to material foundations. Biometric is the usage of a person's novel physiological, lead, and morphological trademark to give valuable person distinguishing proof. Biometric structures that are starting at now available today break down fingerprints, engravings, iris and retina models, and face. Mechanisms that are similar to biometrics anyway are not named such are lead systems, for instance, voice, imprint and keystroke mechanisms. These days biometrics is in effect effectively executed in numerous fields like measurable, security, recognizable proof and approval frameworks.
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Rajvanshi, Saumya, Shiv Chauhan, and Savneet Kaur. "A New Wave in Biometric System: Systematic Study." CGC International Journal of Contemporary Technology and Research 4, no. 2 (August 5, 2022): 300–305. http://dx.doi.org/10.46860/cgcijctr.2022.07.31.300.
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Biometric system is a technique used to identify a person using its personal identification methods. The main concept of biometric systems is to provide confidentiality and security to the user. A number of biometric systems are introduced but some systems are widely used and are famous because of their usage and security they provide. Physiological and Behavioral biometrics are the two types of biometric systems. Biometric systems include physiological biometrics like face recognition, fingerprint recognition, iris recognition and behavioral biometrics like signature recognition and voice recognition. All these recognition systems are discussed in this research paper. Biometric systems work on three levels: Enrollment, Verification, and Identification. Enrollment is the process in which patterns are captured from the user and stored in the database. Verification means to confirm that the sample entered by the user belongs to him or not. When the user wants to access the data then the user must use his/her biometrics so that the system checks that the person who wants to access the data is the real owner of the data or not. This process is identification. All three levels are the working levels of the Biometric System. In earlier years, biometrics were used only at ground levels to provide basic security to data but now the tables have turned. It is playing a major role in providing security to our data. Biometrics are not only used in day-to-day life in phone unlocking, phone assistants, attendance systems but also used at advanced levels like in airports, border security, cloud computing etc. In this research paper, we will discuss the future scope of biometric systems and how it could even change the future.
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Huang, Quan. "Multimodal Biometrics Fusion Algorithm Using Deep Reinforcement Learning." Mathematical Problems in Engineering 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/8544591.
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Multimodal biometrics fusion plays an important role in the field of biometrics. Therefore, this paper presents a multimodal biometrics fusion algorithm using deep reinforcement learning. In order to reduce the influence of user behavior, user’s personal characteristics, and environmental light on image data quality, data preprocessing is realized through data transformation and single-mode biometric image region segmentation. A two-dimensional Gobar filter was used to analyze the texture of local sub-blocks, qualitatively describe the similarity between the filter and the sub-blocks and extract the phase information and local amplitude information of multimodal biometrics features. Deep reinforcement learning was used to construct the classifier of different modal biometrics, and the weighted sum fusion of different modal biometrics was implemented by fractional information. The multimodal biometrics fusion algorithm was designed. The Casia-iris-interval-v4 and NFBS datasets were used to test the performance of the proposed algorithm. The results show that the fused image quality is better, the feature extraction accuracy is between 84% and 93%, the average accuracy of feature classification is 97%, the multimodal biometric classification time is only 110 ms, the multimodal biometric fusion time is only 550 ms, the effect is good, and the practicability is strong.
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Yang, Wencheng, Song Wang, Nor Masri Sahri, Nickson M. Karie, Mohiuddin Ahmed, and Craig Valli. "Biometrics for Internet-of-Things Security: A Review." Sensors 21, no. 18 (September 14, 2021): 6163. http://dx.doi.org/10.3390/s21186163.
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The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.
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Singh, Bhanu, and Nirvisha Singh. "MoLaBSS: Server-Specific Add-On Biometric Security Layer Model to Enhance the Usage of Biometrics." Information 11, no. 6 (June 8, 2020): 308. http://dx.doi.org/10.3390/info11060308.
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With high-paced growth in biometrics, and its easy availability to capture various biometric features, it is emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. Organizations encourage their members to use biometrics, but they are hesitant to use them due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. We propose a server-specific add-on biometric security layer model (MoLaBSS) to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show good potential of this framework to enhance users’ confidence level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.
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Joshi, Amogh. "Future of Cybersecurity: A Study on Biometric Scans." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1180–85. http://dx.doi.org/10.22214/ijraset.2021.38913.
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Abstract: Biometrics is a statistical analysis of people's unique behavioral characteristics. The technology is used for CYBERSECURITY. The basics of biometric authentication is that to stop security breaches by analyzing a person’s unique behavioral characteristics. The term biometrics is derived from the Greek word’s “bios” meaning life and “metricos” meaning to measure. It refers to measurements of physical and biological characteristics of the human body. In this paper, we have studied some biometric methods such as facial recognition, iris recognition, Retinal Recognition, voice recognition. Keywords: Biometrics, Cybersecurity, Biometric Scan, Retinal Scan, Iris Scan, Gait, Voice Recognition.
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Wang, Min, Xuefei Yin, Yanming Zhu, and Jiankun Hu. "Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey." Sensors 22, no. 14 (July 7, 2022): 5111. http://dx.doi.org/10.3390/s22145111.
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Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.
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Ali, Nadir, M. Asghar Khattak, Samina Kanwal, Noreen Farid, Shehrbano Batool, and Mufassar Nishat. "Comparison of Forensic Value of Biometric Analysis in Face & Ear Recognition in the Punjabi Population, Pakistan." Pakistan Journal of Medical and Health Sciences 16, no. 12 (December 31, 2022): 614–16. http://dx.doi.org/10.53350/pjmhs20221612614.
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Background: The use of face biometrics is very prevalent in forensic investigations for the identification of the perpetrators of crime due to the excessive use of CCTV footage that is usually available at the scene of a crime in urban settings. Ear biometric analysis of ear prints is also in vogue as a result of research advancements in the fields of biometrics. Keeping in view this scenario, it is the need of the hour to analyze the forensic value of this type of forensic evidence and compare these two tools of forensic biometrics. Purpose: To analyze and compare the forensic value of biometric analysis of face and ear print recognitions in the Punjabi population, in Punjab, Pakistan. Study design: This study is conducted by collecting the data from 100 samples of different people belonging to different backgrounds from different cities in Punjab, Pakistan after their informed consent and ethical approval. Their facial photographs and ear prints were collected for proceeding biometric analysis to form a database for comparison and recognition. Method and materials: After collecting data, the comparison is done to see whether we can recognize a person by only using ear print analysis or face biometrics after running a search in our own created database. Moreover, we also calculated the forensic values of this biometric analysis separately on its own and combined these two i.e., face and ear biometrics. . Furthermore, standard deviation, F-statistics, and Chitest p-value were also applied to see the power of discrimination of these two biometric methods of identification Results: In 100 samples, face recognition was proved to be 80 % recognition of identity as compared with only ear prints which showed 56 % accuracy in identifying the individuals who participated in this research. Furthermore, the combined result of both face recognition and ear biometrics showed 90 % recognition of the identity of the individuals. Statistical analysis proved that biometric analysis of the face for recognition of the identity of individuals was more valued as compared with ear print recognition. Also, it was found that if we combine these two methods of biometrics, the forensic value of recognition of individuals has increased and showed good results. Conclusion: The forensic value of biometric evidence of face and ear recognition is a very important tool for the forensic identification of individuals in crime scene investigations. Biometric facial recognition is better as compared to only ear print biometric analysis. Furthermore, using face and ear biometrics enhances the forensic value of biometric analysis. Keywords: Biometric analysis, Forensic value, face recognition, ear print recognition,
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Reddy, M. V. Bramhananda, and V. Goutham. "IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION." International Journal of Research -GRANTHAALAYAH 6, no. 1 (January 31, 2018): 80–90. http://dx.doi.org/10.29121/granthaalayah.v6.i1.2018.1596.
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Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.
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Assouma, Abdoul Kamal, Tahirou Djara, and Abdou-Aziz Sobabe. "Multi-Biometrics: Survey and Projection of a New Biometric System." International Journal of Engineering and Advanced Technology 12, no. 3 (February 28, 2023): 80–87. http://dx.doi.org/10.35940/ijeat.c4008.0212323.
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Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our work focuses on multi-origin biometrics which allows the use of pure biometric modalities and metadata in a feature fusion strategy. The main objective of this paper is to present an overview of biometrics as bordered in the literature with a particular focus on multibiometrics and to propose a model of a multi-origin biometric system using pure biometric and soft biometric modalities in a feature-level fusion strategy. The curvelet transformation and the order statistics are proposed respectively for the extraction the feature of the pure biometric modalities, and for the selection of the relevant feature of each modality in order to ensure a good level of discrimination of the individuals. In this paper, we have presented the overview of biometrics through its concepts, modalities, advantages, disadvantages and implementation architectures. A focus has been put on multi-biometrics with the presentation of a harmonized process for feature fusion. For the experiments, we proposed a global model for feature fusion in a multi-origin system using face and iris modalities as pure biometrics, and facial skin color as metadata. This system and the results will be presented in future work.
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Kim, Min-Gu, Hae-Min Moon, Yongwha Chung, and Sung Bum Pan. "A Survey and Proposed Framework on the Soft Biometrics Technique for Human Identification in Intelligent Video Surveillance System." Journal of Biomedicine and Biotechnology 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/614146.
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Biometrics verification can be efficiently used for intrusion detection and intruder identification in video surveillance systems. Biometrics techniques can be largely divided into traditional and the so-called soft biometrics. Whereas traditional biometrics deals with physical characteristics such as face features, eye iris, and fingerprints, soft biometrics is concerned with such information as gender, national origin, and height. Traditional biometrics is versatile and highly accurate. But it is very difficult to get traditional biometric data from a distance and without personal cooperation. Soft biometrics, although featuring less accuracy, can be used much more freely though. Recently, many researchers have been made on human identification using soft biometrics data collected from a distance. In this paper, we use both traditional and soft biometrics for human identification and propose a framework for solving such problems as lighting, occlusion, and shadowing.
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Sayoud, Halim. "Biometrics." International Journal of Technoethics 2, no. 1 (January 2011): 19–34. http://dx.doi.org/10.4018/jte.2011010102.
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The term biometrics is derived from the Greek words: bio (life) and metrics (to measure). “Biometric technologies” are defined as automated methods of verifying or recognizing the identity of a living person based on a physiological or behavioral characteristic. Several techniques and features were used over time to recognize human beings several years before the birth of Christ. Today, this research field has become very employed in many applications such as security applications, multimedia applications and banking applications. Also, many methods have been developed to strengthen the biometric accuracy and reduce the imposture errors by using several features such as face, speech, iris, finger vein, etc. From a security purpose and economic point of view, biometrics has brought a great benefit and has become an important tool for governments and institutions. However, citizens are expressing their thorough worry, which is due to the freedom limitations and loss of privacy. This paper briefly presents some new technologies that have recently been proposed in biometrics with their levels of reliability, and discusses the different social and ethic problems that may result from the abusive use of these technologies.
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K. P. Ajitha, Gladis, and D. Sharmila. "Systematic digital signal processing approach in various biometric identification." i-manager's Journal on Digital Signal Processing 10, no. 2 (2022): 7. http://dx.doi.org/10.26634/jdp.10.2.19290.
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Biometrics are unique physical characteristics, such as fingerprints, that can be used for automatic recognition. Biometric identifiers are often classified as physiological characteristics associated with body shape. The goal is to capture a piece of biometric data from that person. It could be a photograph of their face, a recording of their voice, or a picture of their fingerprints. While there are numerous types of biometrics for authentication, the six most common are facial, voice, iris, near-field communication, palm or finger vein patterns, and Quick Response (QR) code. Biometrics is a subset of the larger field of human identification science. This paper explores computational approaches to speaker recognition, face recognition, speech recognition, and fingerprint recognition to assess the overall state of digital signal processing in biometrics.
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S. Raju, A., and V. Udayashankara. "A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 689. http://dx.doi.org/10.14419/ijet.v7i4.36.24224.
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Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner for those researchers, who wish to persue their work in the area of biometrics.
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Chinyemba, Melissa K., and Jackson Phiri. "Gaps in the Management and Use of Biometric Data: A Case of Zambian Public and Private Institutions." Zambia ICT Journal 2, no. 1 (June 29, 2018): 35–43. http://dx.doi.org/10.33260/zictjournal.v2i1.49.
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The current physical and cybersecurity systems rely on traditional three-factor authentication to mitigate the threats posed by insider attacks. Key is the use of biometric information. Biometrics are a unique measurement and analysis of the unique physiological special traits such as voice, eye structure and others that can be used in the discipline of varying person identification. Biometry, which is the analysis of these biometrics is a complex process but guarantees identification and non-repudiation. If used to identify humans then several issues such as where is the biometric data stored? Who has access to it? And how does one ensure that such data satisfies the principle of availability. To achieve availability, secure transportation arises. To achieve transportation, non-repudiation, confidentiality and authentication, integrity arise. A storage and transport system is recommended to these challenges. In this paper, we explore the gaps into how public and private institution store and manage biometrics information. We benchmarked each organization again the ISO 30107 and ISO 24745. Our results show that while most companies are adopting and using biometrics systems, few have adopted the ISO biometrics standards that govern the storage and management of biometric information and hence creating security risk.
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Lee, Boo-Ha, and Shin-Uk Park. "Legislative Policy Consideration for Reinforcement of Biometrics Protection." LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY 13, no. 1 (June 30, 2022): 171–98. http://dx.doi.org/10.34267/cbstl.2022.13.1.171.
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Article 23 (1) of the Personal Information Protection Act stipulates that “A personal information controller shall not process any information prescribed by Presidential Decree (hereinafter referred to as ‘sensitive data’), including ideology, belief, admission to or withdrawal from a trade union or political party, political opinions, health, sex life, and other personal information that is likely to markedly threaten the privacy of any data subject.” Article 18 of the Enforcement Decree of the Personal Information Protection Act stipulates that ‘Information prescribed by Presidential Decree’ in the main clause , with the exception of the subparagraph, of Article 23 (1) of the Act means the following data or information. In subparagraph 3, “Personal information resulting from specific technical processing of data relating to the physical, physiological or behavioral characteristics of an individual for the purpose of uniquely identifying that individual” is defined as one of the sensitive data.
The range of sensitive data is wider than that of biometrics. ‘Data that constitutes a criminal history record’ defined in subparagraph 5 of Article 2 of the Act on the Lapse of Criminal Sentences, etc. as stipulated in Article 18 (3) of the Enforcement Decree of the Personal Information Protection Act and Article 18 (4) of the Enforcement Decree of the Personal Information Protection Act ‘Personal information revealing racial or ethnic origin’ is sensitive data completely different from biometric information.
Therefore, it is necessary to enact a separate law to protect and manage biometrics or biometric information that requires more protection than sensitive data.
As safety measures for biometrics security, there are first, security measures for forged/falsified biometric information, second, protection of the transmission section when collecting and inputting biometric information, third, use within the scope of the agreed purpose, fourth, biometric information collection and input processing at the terminal, fifth, encryption when storing biometric information, sixth, destruction of biometric information, seventh, separate storage when storing original biometric information, eighth, in case of leakage of biometric information, protective measures are taken.
The Act on Protection and Management of Biometrics (draft) includes Chapter 1 General Provisions, Chapter 2 Establishment of Biometrics Protection Policy, Chapter 3 Collection and Use of Biometrics and Restrictions on It, Chapter 4 Safe Management of Biometrics, and Chapter 5, Guarantee of Rights of Data Subjects.
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Gupta, Ms Neha. "A New Wave in Biometric System: Systematic Study incorporated with Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 10, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33563.
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Biometric system is a technique used to identify a person using its personal identification methods. The main concept of biometric systems is to provide confidentiality and security to the user. A number of biometric systems are introduced but some systems are widely used and are famous because of their usage and security they provide. Physiological and Behavioral biometrics are the two types of biometric systems. Biometric systems include physiological biometric like face recognition, finger print recognition, ir is recognition and behavioral biometrics like signature recognition and voice recognition. All these recognition systems are discussed in this research paper. Biometric systems work on three levels: Enrollment, Verification, and Identification. Enrollment is the process in which patterns are captured from the user and stored in the database. Verification means to confirm that the sample entered by the user belongs to him or not. When the user wants to access the data then the user must use his/her biometrics that the systematic hacks that the person who wants to access the data is the real owner of the data or not. This process is identification. All three levels are the working levels of the Biometric System. In earlier years, biometrics was used only at ground levels to provide basic security to data but now the tables have turned. It is playing a major role in providing security to our data. Biometrics are not only used in day-to-day life in phone unlocking, phone assistants, attendance systems but also used at advanced levels like in airports, border security, cloud computing etc. In this research paper, we will discuss the future scope of biometric systems and how it could even change the future.
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Fianyi, Israel, and Tanveer Zia. "Biometric Technology Solutions to Countering Today's Terrorism." International Journal of Cyber Warfare and Terrorism 6, no. 4 (October 2016): 28–40. http://dx.doi.org/10.4018/ijcwt.2016100103.
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The purpose of this paper is to examine the relevance of biometric technologies in increasing the fight against terrorism and other related border security challenges such as identity dominance. Since the 11th September, 2001 catastrophe in USA the need for biometrics technology for identification purpose has become important. Consequently, the many ripostes that were renewed on identity management included enhanced use of biometrics to verify and authenticate travellers at various airports as well as the use of video surveillance equipped with facial recognition sensors. The growth in data and storage devices have also become a critical phenomenon in biometrics deployment, the swiftness and accuracy with which these biometrics details can be processed is a prevailing challenge. This paper provides an extensive review of literatures on prospects of biometric technologies and other interrelated technologies in the fight against terrorism. To date, there is relatively meagre academic research examining how biometric technologies enhance border security as well as individual security.
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Drosou, A., D. Ioannidis, K. Moustakas, and D. Tzovaras. "Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept." Scientific World JOURNAL 11 (2011): 503–19. http://dx.doi.org/10.1100/tsw.2011.51.
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Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.
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A. El_Rahman, Sahar, and Ala Saleh Alluhaidan. "Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments." PLOS ONE 19, no. 2 (February 15, 2024): e0291084. http://dx.doi.org/10.1371/journal.pone.0291084.
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In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.
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Alam*, Varisha, and Dr Mohammad Arif. "Classification of Large Biometric Data in Database System." International Journal of Innovative Technology and Exploring Engineering 10, no. 10 (August 30, 2021): 1–8. http://dx.doi.org/10.35940/ijitee.d8592.08101021.
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"Biometrics" is got from the Greek word 'life' and 'measure' which implies living and evaluation take apart. It simply converts into "life estimation". Biometrics uses computerized acknowledgment of people, dependent on their social and natural attributes. Biometric character are data separated from biometric tests, which can use for examination with a biometric orientation. Biometrics involves techniques to unusually recognize people dependent on at least one inherent physical or behavior attribute. In software engineering, specifically, biometric is used as a form of character retrieve the Committee and retrieve command. Biometric identically utilized to recognize people in bunches that are in observation. Biometric has quickly risen like a auspicious innovation for validation and has effectively discovered a spot in most of the scientific safety regions. An effective bunching method suggest for dividing enormous biometrics data set through recognizable proof. This method depends on the changed B+ tree is decreasing the discs get to. It diminishes the information recovery time and also possible error rates. Hence, for bigger applications, the need to reduce the data set to a more adequate portion emerges to accomplish both higher paces and further developed precision. The main motivation behind ordering is to recover a small data set for looking through the inquiry
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Sharma, Puneet. "Biometrics and Credit Cards: Fingerprints, Faces, and Voices as Your Digital Bodyguards." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (December 30, 2024): 1–6. https://doi.org/10.55041/ijsrem14234.
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In an era of rampant digitalization, the integration of biometrics with credit card technology has emerged as a revolutionary approach to secure transactions. From fingerprints to facial recognition and voice authentication, these digital bodyguards offer a robust, personalized layer of protection against unauthorized access and fraud. Biometrics not only enhances security but also simplifies user experiences, making transactions swift and frictionless. This paper delves into the multifaceted role of biometrics in credit card security, exploring key technologies, their implementation challenges, and their transformative impact on the financial landscape. With a focus on areas such as biometric data encryption, spoof resistance, and privacy-preserving authentication, it also addresses concerns about data misuse and regulatory compliance. As financial institutions and technology providers converge on this frontier, biometrics promises to redefine trust and convenience in payment systems. Keywords Biometrics, Credit Card Security, Fingerprint Authentication, Facial Recognition, Voice Authentication, Biometric Data Encryption, Spoof Resistance, Privacy-Preserving Authentication, Financial Technology, User Experience Optimization
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Jain, Anil K., and Arun Ross. "Bridging the gap: from biometrics to forensics." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1674 (August 5, 2015): 20140254. http://dx.doi.org/10.1098/rstb.2014.0254.
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Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large.
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Ladhiya, Karan. "Privacy Preserving Bio-Metric Authentication." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 114–24. http://dx.doi.org/10.22214/ijraset.2022.40570.
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Abstract: Biometric authentication is now extensively used in several systems and applications to authenticate users using their biometrics. The biometrics of the users are compared to the biometric templates already stored on the server, and if there is a match, only the user is permitted to enter the system. However, because each user's biometrics are unique, it is more important than the user's actual biometric data is never leaked. Moreover, the utilization of the user's actual biometric data for comparisons during the authentication process can't be done because the revelation of the user's actual biometrics to the server should not be done. Throughout authentication, each user will encrypt his biometrics and then transmit this encrypted data to the server for comparison, and this data will never be decrypted for privacy reasons during the whole authentication process. To compare two encrypted data without decrypting them, the present study uses the homomorphic properties of the Pailler cryptosystem which will be the encryption of the algorithm for the comparison part. The use of Euclidean Distance is made to find the squared distance between the users’ queried feature vector and the templates stored into the server. In the end, among all the distances, the minimum distance will be chosen and will compare with some predefined threshold to decide whether the user is an authenticated user or not.
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Hausawi, Yasser. "Role of Usability on using Biometrics for Cybersecurity." Transactions on Networks and Communications 7, no. 4 (November 8, 2019): 19–26. http://dx.doi.org/10.14738/tnc.74.7244.
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ABSTRACT
Biometrics are traits that allow individuals to be identified. Popular biometrics include fingerprints, faces, and irides. A common use of biometric systems is for authentication of users desiring access to a system or resource. However, the use of biometrics presents challenges and opportunities unique to other authentication methods, such as passwords and tokens. Biometric systems are also vulnerable to poor usability. Such systems must be engineered with wide user accessibility and acceptability in mind, but must still provide robust security as well. As lack of usability causes systems' failures, and enhancing systems' usability reduces such failures. This article first presents an overview of biometric systems employed today, including their usage and security merits. We then consider the specific role usability plays on both the development and long-term utility of biometric systems used for Cybersecurity.
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Wevers, Rosa. "Unmasking Biometrics’ Biases." TMG Journal for Media History 21, no. 2 (November 1, 2018): 89. http://dx.doi.org/10.18146/2213-7653.2018.368.
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The article investigates the role of identity and the body in biometric technologies, contesting the conception that biometrics are neutral. It discusses biometrics’ exclusionary effects with regards to gender, race, class and ability, among others, by unveiling its historical links to nineteenth-century pseudoscientific practices. It does so through an analysis of Zach Blas’ Facial Weaponization Suite, an artistic critique of this dominant conception that draws attention to biometrics’ contested history and its current implications for marginalised identities.
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Channegowda, Arjun Benagatte, and H. N. Prakash. "Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (April 1, 2021): 187. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp187-195.
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Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.
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Hashem, Mithak Ibrahim, and Kadhim Hasen Kuban. "Key generation method from fingerprint image based on deep convolutional neural network model." Nexo Revista Científica 36, no. 06 (December 31, 2023): 906–25. http://dx.doi.org/10.5377/nexo.v36i06.17447.
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Biometrics effect our live. Security applications employ biometrics. Biometric encryption is growing. Encryption requires biometric key creation. Long, random, and unexpected is the key. Information and communication security research emphasizes long, strong encryption keys. The proposed system uses fingerprint biometrics to generate a long, random biometric encryption key for symmetric encryption. Pre-processing removed noise from donor fingerprint images in the dataset. The program then trains an updateable Tuned VGG-16 convolutional neural network model and tests it on fingerprint images to learn fundamental fingerprint properties. The convolutional neural netwoprk CNN model retains the final weights for the second model to extract encryption key features. Transfer learning built a second convolutional neural network model to retrieve features without relearning. Keeping vector mean for processing. The last step generates an encryption key based on each person's vector of unique biometric features can be used for symmetric encryption algorithms to encrypt personal documents on the personal PC or personal cloud. Our CNN based method uses biometrics to recognize people and create safe and trustworthy encryption keys with over 99% accuracy in testing. Our 98%-accurate deep ANN classifier exceeds the support vector machine and random forest classifiers.
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Thian Song, Ong, Andrew Teoh Beng Jin, and Tee Connie. "Personalized biometric key using fingerprint biometrics." Information Management & Computer Security 15, no. 4 (August 21, 2007): 313–28. http://dx.doi.org/10.1108/09685220710817824.
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Salama, Gerges M., Safaa El-Gazar, Basma Omar, Rana M. Nassar, Ashraf A. M. Khalaf, Ghada M. El-banby, Hesham F. A. Hamed, Walid El-shafai, and Fathi E. Abd el-samie. "Cancelable biometric system for IoT applications based on optical double random phase encoding." Optics Express 30, no. 21 (September 28, 2022): 37816. http://dx.doi.org/10.1364/oe.466101.
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The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security. To access and control sensitive environments like IoT, passwords are not recommended for high security levels. Biometrics can be used instead, but more protection is needed to store original biometrics away from invaders. This paper presents a cancelable multimodal biometric recognition system based on encryption algorithms and watermarking. Both voice-print and facial images are used as individual biometrics. Double Random Phase Encoding (DRPE) and chaotic Baker map are utilized as encryption algorithms. Verification is performed by estimating the correlation between registered and tested models in their cancelable format. Simulation results give Equal Error Rate (EER) values close to zero and Area under the Receiver Operator Characteristic Curve (AROC) equal to one, which indicates the high performance of the proposed system in addition to the difficulty to invert cancelable templates. Moreover, reusability and diversity of biometric templates is guaranteed.
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Yampolskiy, Roman V., Nawaf Ali, Darryl D'Souza, and Abdallah A. Mohamed. "Behavioral Biometrics." International Journal of Natural Computing Research 4, no. 3 (July 2014): 85–118. http://dx.doi.org/10.4018/ijncr.2014070105.
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This work categorizes and reviews behavioral biometrics with the inclusion of future-oriented techniques. A general introduction to this field is given alongside the benefits of this non-intrusive approach. It presents the examination and analysis of the current research in the field and the different types of behavior-centric features. Accuracy rates for verifying users with different behavioral biometric approaches are compared. Privacy issues that will or may arise in the future with behavioral biometrics are also addressed. Finally, the general properties of behavior, the influence of environmental factors on observed behavior and the potential directions for future research in the field of behavioral biometrics are discussed.
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Mróz-Gorgoń, Barbara, Wojciech Wodo, Anna Andrych, Katarzyna Caban-Piaskowska, and Cyprian Kozyra. "Biometrics Innovation and Payment Sector Perception." Sustainability 14, no. 15 (August 1, 2022): 9424. http://dx.doi.org/10.3390/su14159424.
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This paper presents an analysis of innovations in the biometrics market, which have started to play a very important role in personal identification and identification systems. The aim of the study was to analyze current customs and opinions regarding payment methods, as well as to identify threats and opportunities for new biometric solutions in this area. First, the history of the biometrics market is presented. Acceptance patterns of new technologies are explored and modified. The authors used literature reviews, qualitative research (focus groups), and quantitative research (questionnaire survey) as methods. The main value and importance of biometrics is the uniqueness of biometric patterns (e.g., face, fingerprint, iris, etc.), which takes the security of these systems to a new level. The results of the quantitative study based on the qualitative survey show positive verification of the hypothesized reasons; e.g., importantly, that the age of potential users of biometric payments influences the fear about personal data. Fear of losing personal data affects the perceived safety of biometric payments. Perceived security has a very strong influence on attitudes towards biometric payments, which is the strongest predictor of behavioral intention to use biometric payments.
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Arunarani, S., and R. Gobinath. "A survey on multimodal biometrics for human authentication." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 273. http://dx.doi.org/10.14419/ijet.v7i2.33.14167.
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Authentication process identifies an individual to get an endorsed access by entering their login credentials. The inconvenience with this method is the user must remember the keywords, and the passwords can be predicted or if it is hard to guess it will be cracked through brute force. Due to this fault, this method is lack of integrity. Biometrics sample recognize a person based on his behavioral or physiological char-acteristics. Unimodal biometric systems have to resist with a different types of problems such as inconsistent data, intra-class variations, deceit attacks and high error rates. Multimodal biometrics implements secure authentication using various biometric traits. This survey gives us a wide scope for improving and enhancing the biometric applications. In this paper, we have explained multimodal biometrics to decrease the error rate and increase the security.
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Buciu, Ioan, and Alexandru Gacsadi. "Biometrics Systems and Technologies: A survey." International Journal of Computers Communications & Control 11, no. 3 (March 24, 2016): 315. http://dx.doi.org/10.15837/ijccc.2016.3.2556.
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In a nutshell, a biometric security system requires a user to provide some biometric features which are then verified against some stored biometric templates. Nowadays, the traditional password based authentication method tends to be replaced by advanced biometrics technologies. Biometric based authentication is becoming increasingly appealing and common for most of the human-computer interaction devices. To give only one recent example, Microsoft augmented its brand new Windows 10 OS version with the capability of supporting face recognition when the user login in. This chapter does not intend to cover a comprehensive and detailed list of biometric techniques. The chapter rather aims at briefly discussing biometric related items, including principles, definitions, biometric modalities and technologies along with their advantages, disadvantages or limitations, and biometric standards, targeting unfamiliar readers. It also mentions the attributes of a biometric system as well as attacks on biometrics. Important reference sources are pointed out so that the interested reader may gain deeper in-depth knowledge by consulting them.
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Salman, Duha D., Raghad A. Azeez, and Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm." Engineering and Technology Journal 38, no. 3B (December 25, 2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.
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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence. Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys. Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.
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Salman, Duha D., Raghad A. Azeez, and Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm." Engineering and Technology Journal 38, no. 3B (December 25, 2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.
Full textAbstract:
Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence. Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys. Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.
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Thakor, Kiran B. "Comparative Analysis of Vein Biometrics Methodologies: A Comprehensive Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5365–73. http://dx.doi.org/10.22214/ijraset.2023.52830.
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Abstract: Vein biometrics has emerged as a promising modality for secure and reliable personal identification. With its unique characteristics and inherent physiological properties, veins offer distinct advantages over other biometric modalities. However, the methodology employed in vein biometrics plays a crucial role in determining its performance and accuracy. This paper presents a comprehensive comparison of various methodologies used in vein biometrics, aiming to provide insights into the strengths, weaknesses, and advancements in this field.
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NAZAR, AKIF, ISSA TRAORÉ, and AHMED AWAD E. AHMED. "INVERSE BIOMETRICS FOR MOUSE DYNAMICS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 461–95. http://dx.doi.org/10.1142/s0218001408006363.
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Various techniques have been proposed in different literature to analyze biometric samples collected from individuals. However, not a lot of attention has been paid to the inverse problem, which consists of synthesizing artificial biometric samples that can be used for testing existing biometric systems or protecting them against forgeries. In this paper, we present a framework for mouse dynamics biometrics synthesis. Mouse dynamics biometric is a behavioral biometric technology, which allows user recognition based on the actions received from the mouse input device while interacting with a graphical user interface. The proposed inverse biometric model learns from random raw samples collected from real users and then creates synthetic mouse actions for fake users. The generated mouse actions have unique behavioral properties separate from the real mouse actions. This is shown through various comparisons of behavioral metrics as well as a Kolmogorov–Smirnov test. We also show through a two-fold cross-validation test that by submitting sample synthetic data to an existing mouse biometrics analysis model we achieve comparable performance results as when the model is applied to real mouse data.
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OMOTOSHO, LAWRENCE, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, and JOSHUA OYENIYI. "AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK." Journal of Engineering Studies and Research 27, no. 2 (October 10, 2021): 73–81. http://dx.doi.org/10.29081/jesr.v27i2.276.
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Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.
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Sedik, Ahmed, Ahmed A. Abd El-Latif, Mohammed El-Affendi, and Hala Mostafa. "A Cancelable Biometric System Based on Deep Style Transfer and Symmetry Check for Double-Phase User Authentication." Symmetry 15, no. 7 (July 15, 2023): 1426. http://dx.doi.org/10.3390/sym15071426.
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In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks.
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Kostyuk, Nataliya, Phyadragren Cole, Natarajan Meghanathan, Raphael D. Isokpehi, and Hari H. P. Cohly. "Gas Discharge Visualization: An Imaging and Modeling Tool for Medical Biometrics." International Journal of Biomedical Imaging 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/196460.
Full textAbstract:
The need for automated identification of a disease makes the issue of medical biometrics very current in our society. Not all biometric tools available provide real-time feedback. We introduce gas discharge visualization (GDV) technique as one of the biometric tools that have the potential to identify deviations from the normal functional state at early stages and in real time. GDV is a nonintrusive technique to capture the physiological and psychoemotional status of a person and the functional status of different organs and organ systems through the electrophotonic emissions of fingertips placed on the surface of an impulse analyzer. This paper first introduces biometrics and its different types and then specifically focuses on medical biometrics and the potential applications of GDV in medical biometrics. We also present our previous experience with GDV in the research regarding autism and the potential use of GDV in combination with computer science for the potential development of biological pattern/biomarker for different kinds of health abnormalities including cancer and mental diseases.