Zeitschriftenartikel: „EKG biometrie“

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Singh, Yogendra Narain, Sanjay Kumar Singh und Amit Kumar Ray. „Bioelectrical Signals as Emerging Biometrics: Issues and Challenges“. ISRN Signal Processing 2012 (26.07.2012): 1–13. http://dx.doi.org/10.5402/2012/712032.

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This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

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Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, Noubeil Guermat, Lahcene Ziet, Mohamed Cheniti und Khan Muhammad. „Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition“. Symmetry 12, Nr. 3 (10.03.2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.

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Damaševičius, Robertas, Rytis Maskeliūnas, Egidijus Kazanavičius und Marcin Woźniak. „Combining Cryptography with EEG Biometrics“. Computational Intelligence and Neuroscience 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1867548.

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Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

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Yin, Jing Hai, Zheng Dong Mu und Jian Feng Hu. „Design of Identification System Based on BCI“. Applied Mechanics and Materials 496-500 (Januar 2014): 1975–78. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1975.

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Nowadays EEG-based identification biometric research becomes a new topic. Due to the current biometric technology are still exist some problems, therefore, an urgent needed is to find a new biometric technology. In this paper, we use EEG signals as biometrics, which is home to a new biometric research trends, cutting-edge and exploratory nature, there is a good prospect. We establish a set of EEG-based identification systems and medium-sized EEG signatures, explore effective methods of analysis biometric technology to make up for past deficiencies, to further improve and develop the theory of biometric technologies and applications for EEG-based identification technology promotion foundation.

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S. Raju, A., und V. Udayashankara. „A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques“. International Journal of Engineering & Technology 7, Nr. 4.36 (09.12.2018): 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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Elshahed, Marwa A. „Personal identity verification based ECG biometric using non-fiducial features“. International Journal of Electrical and Computer Engineering (IJECE) 10, Nr. 3 (01.06.2020): 3007. http://dx.doi.org/10.11591/ijece.v10i3.pp3007-3013.

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Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

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Kim, Hanvit, Haena Kim, Se Chun, Jae-Hwan Kang, Ian Oakley, Youryang Lee, Jun Ryu et al. „A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors“. Sensors 18, Nr. 8 (20.08.2018): 2738. http://dx.doi.org/10.3390/s18082738.

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Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD . 1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD . 1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

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Jain, Rubal, und Chander Kant. „Attacks on Biometric Systems: An Overview“. International Journal of Advances in Scientific Research 1, Nr. 7 (03.09.2015): 283. http://dx.doi.org/10.7439/ijasr.v1i7.1975.

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Biometrics is a pattern recognition system that refers to the use of different physiological (face, fingerprints, etc.) and behavioral (voice, gait etc.) traits for identification and verification purposes. A biometrics-based personal authentication system has numerous advantages over traditional systems such as token-based (e.g., ID cards) or knowledge-based (e.g., password) but they are at the risk of attacks. This paper presents a literature review of attack system architecture and makes progress towards various attack points in biometric system. These attacks may compromise the template resulting in reducing the security of the system and motivates to study existing biometric template protection techniques to resist these attacks.

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Hagras, Shaimaa, Reham R. Mostafa und Ahmed Abou elfetouh. „A Biometric System Based on Single-channel EEG Recording in One-second“. International Journal of Intelligent Systems and Applications 12, Nr. 5 (08.10.2020): 28–40. http://dx.doi.org/10.5815/ijisa.2020.05.03.

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In recent years, there are great research interests in using the Electroencephalogram (EEG) signals in biometrics applications. The strength of EEG signals as a biometric comes from its major fraud prevention capability. However, EEG signals are so sensitive, and many factors affect its usage as a biometric; two of these factors are the number of channels, and the required time for acquiring the signal; these factors affect the convenience and practicality. This study proposes a novel approach for EEG-based biometrics that optimizes the channels of acquiring data to only one channel. And the time to only one second. The results are compared against five commonly used classifiers named: KNN, Random Forest (RF), Support Vector Machine (SVM), Decision Tables (DT), and Naïve Bayes (NB). We test the approach on the public Texas data repository. The results prove the constancy of the approach for the eight minutes. The best result of the eyes-closed scenario is Average True Positive Rate (TPR) 99.1% and 98.2% for the eyes-opened. And it reaches 100% for multiple subjects.

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Tirado-Martin, Paloma, und Raul Sanchez-Reillo. „BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets“. Applied Sciences 11, Nr. 13 (24.06.2021): 5880. http://dx.doi.org/10.3390/app11135880.

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Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

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Chou, Ching-Yao, Yo-Woei Pua, Ting-Wei Sun und An-Yeu (Andy) Wu. „Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis“. Sensors 20, Nr. 11 (09.06.2020): 3279. http://dx.doi.org/10.3390/s20113279.

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Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

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Hendrawan, Muhammad Afif, Pramana Yoga Saputra und Cahya Rahmad. „Identification of optimum segment in single channel EEG biometric system“. Indonesian Journal of Electrical Engineering and Computer Science 23, Nr. 3 (01.09.2021): 1847. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1847-1854.

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Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

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Alhayajneh, Abdullah, Alessandro Baccarini, Gary Weiss, Thaier Hayajneh und Aydin Farajidavar. „Biometric Authentication and Verification for Medical Cyber Physical Systems“. Electronics 7, Nr. 12 (14.12.2018): 436. http://dx.doi.org/10.3390/electronics7120436.

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A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g., ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

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M. Jomaa, Rami, Hassan Mathkour, Yakoub Bazi und Md Saiful Islam. „End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection“. Sensors 20, Nr. 7 (07.04.2020): 2085. http://dx.doi.org/10.3390/s20072085.

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Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

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Sridevi, T., P. Mallikarjuna Rao und P. V. Ramaraju. „Wireless sensor data mining for e-commerce applications“. Indonesian Journal of Electrical Engineering and Computer Science 14, Nr. 1 (25.12.2018): 462. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp462-470.

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Information hiding is the most important criteria today in several sectors, due to security issues. Mostly for the security applications used in Finance & banking sectors, hiding the information about users and their transactions are necessary at present from the hackers in all high security zones. In this consequence biometrics is progressively considered as foundation component for an extensive array of personal authentication solutions, both at the national level (E.g. India UIDAI) and the smaller-scale (E.g. banking ATMs, school lunch payment systems). Biometric fraud is also an area of increasing concern, as the number of deployed biometric systems increases and fraudsters become aware of the potential to compromise them. Organizations are increasingly deploying process and technology solutions to stay one step ahead. At present Bankers are using different single Biometric Modalities for different services. All Biometric features are not suitable, for all services because of various artifacts while extracting features from the sensors due to background noise, lighting conditions, ease of access etc. This paper proposes a multi model system that will show a onetime single solution to meet all their security problems. This paper particularly handles how to incorporate cryptography and steganography in biometric applications.

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Thomas, Kavitha P., und A. P. Vinod. „Toward EEG-Based Biometric Systems: The Great Potential of Brain-Wave-Based Biometrics“. IEEE Systems, Man, and Cybernetics Magazine 3, Nr. 4 (Oktober 2017): 6–15. http://dx.doi.org/10.1109/msmc.2017.2703651.

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Ferrag, Mohamed Amine, Leandros Maglaras und Abdelouahid Derhab. „Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends“. Security and Communication Networks 2019 (05.05.2019): 1–20. http://dx.doi.org/10.1155/2019/5452870.

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Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.

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Qin, Huafeng, und Peng Wang. „A Template Generation and Improvement Approach for Finger-Vein Recognition“. Information 10, Nr. 4 (18.04.2019): 145. http://dx.doi.org/10.3390/info10040145.

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Finger-vein biometrics have been extensively investigated for person verification. One of the open issues in finger-vein verification is the lack of robustness against variations of vein patterns due to the changes in physiological and imaging conditions during the acquisition process, which results in large intra-class variations among the finger-vein images captured from the same finger and may degrade the system performance. Despite recent advances in biometric template generation and improvement, current solutions mainly focus on the extrinsic biometrics (e.g., fingerprints, face, signature) instead of intrinsic biometrics (e.g., vein). This paper proposes a weighted least square regression based model to generate and improve enrollment template for finger-vein verification. Driven by the primary target of biometric template generation and improvement, i.e., verification error minimization, we assume that a good template has the smallest intra-class distance with respect to the images from the same class in a verification system. Based on this assumption, the finger-vein template generation is converted into an optimization problem. To improve the performance, the weights associated with similarity are computed for template generation. Then, the enrollment template is generated by solving the optimization problem. Subsequently, a template improvement model is proposed to gradually update vein features in the template. To the best of our knowledge, this is the first proposed work of template generation and improvement for finger-vein biometrics. The experimental results on two public finger-vein databases show that the proposed schemes minimize the intra-class variations among samples and significantly improve finger-vein recognition accuracy.

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Kumar, Sunil. „ECG Biometric Identification“. International Journal for Research in Applied Science and Engineering Technology 6, Nr. 3 (31.03.2018): 2148–52. http://dx.doi.org/10.22214/ijraset.2018.3505.

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Agrafioti, Foteini, Francis M. Bui und Dimitrios Hatzinakos. „Secure Telemedicine: Biometrics for Remote and Continuous Patient Verification“. Journal of Computer Networks and Communications 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/924791.

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The technological advancements in the field of remote sensing have resulted in substantial growth of the telemedicine industry. While health care practitioners may now monitor their patients’ well-being from a distance and deliver their services remotely, the lack of physical presence introduces security risks, primarily with regard to the identity of the involved parties. The sensing apparatus, that a patient may employ at home, collects and transmits vital signals to medical centres which respond with treatment decisions despite the lack of solid authentication of the transmitter’s identity. In essence, remote monitoring increases the risks of identity fraud in health care. This paper proposes a biometric identification solution suitable for continuous monitoring environments. The system uses the electrocardiogram (ECG) signal in order to extract unique characteristics which allow to discriminate users. In security, ECG falls under the category ofmedical biometrics, a relatively young but promising field of biometric security solutions. In this work, the authors investigate the idiosyncratic properties of home telemonitoring that may affect the ECG signal and compromise security. The effects of psychological changes on the ECG waveform are taken into consideration for the design of a robust biometric system that can identify users based on cardiac signals despite physical or emotional variations.

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Yang, Wencheng, Song Wang, Jiankun Hu, Ahmed Ibrahim, Guanglou Zheng, Marcelo Jose Macedo, Michael N. Johnstone und Craig Valli. „A Cancelable Iris- and Steganography-Based User Authentication System for the Internet of Things“. Sensors 19, Nr. 13 (06.07.2019): 2985. http://dx.doi.org/10.3390/s19132985.

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Remote user authentication for Internet of Things (IoT) devices is critical to IoT security, as it helps prevent unauthorized access to IoT networks. Biometrics is an appealing authentication technique due to its advantages over traditional password-based authentication. However, the protection of biometric data itself is also important, as original biometric data cannot be replaced or reissued if compromised. In this paper, we propose a cancelable iris- and steganography-based user authentication system to provide user authentication and secure the original iris data. Most of the existing cancelable iris biometric systems need a user-specific key to guide feature transformation, e.g., permutation or random projection, which is also known as key-dependent transformation. One issue associated with key-dependent transformations is that if the user-specific key is compromised, some useful information can be leaked and exploited by adversaries to restore the original iris feature data. To mitigate this risk, the proposed scheme enhances system security by integrating an effective information-hiding technique—steganography. By concealing the user-specific key, the threat of key exposure-related attacks, e.g., attacks via record multiplicity, can be defused, thus heightening the overall system security and complementing the protection offered by cancelable biometric techniques.

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Lai, Chi Qin, Haidi Ibrahim, Mohd Zaid Abdullah, Jafri Malin Abdullah, Shahrel Azmin Suandi und Azlinda Azman. „Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification“. Computational Intelligence and Neuroscience 2019 (02.06.2019): 1–10. http://dx.doi.org/10.1155/2019/7895924.

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Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.

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Giełczyk, Agata, und Michał Choraś. „Intelligent human-centred mobile authentication system based on palmprints“. Journal of Intelligent & Fuzzy Systems 39, Nr. 6 (04.12.2020): 8217–24. http://dx.doi.org/10.3233/jifs-189142.

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Biometrics, as an intelligent and secure authentication method, has recently become increasingly popular. Modern society uses fingerprints, iris and face recognition on a daily basis, even on a large scale; for example, in biometric passports. However, there are still other biometric traits that may provide sufficiently high accuracy but have not been widely implemented so far, e.g. palmprints. In this article, we propose a novel human-centred method of palmprint-based user verification. The proposed method is dedicated to the mobile devices and provides the accuracy reaching 94.5%. Moreover, the method is time-computing efficient and gives the response in less than 0.2 s. All the experiments described in the article were performed using the benchmark PolyU database and three widely available mobile phones.

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Moujahdi, Chouaib, George Bebis, Sanaa Ghouzali, Mounia Mikram und Mohammed Rziza. „Biometric Template Protection Using Spiral Cube: Performance and Security Analysis“. International Journal on Artificial Intelligence Tools 25, Nr. 01 (Februar 2016): 1550027. http://dx.doi.org/10.1142/s021821301550027x.

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Personal authentication systems based on biometrics have given rise to new problems and challenges related to the protection of personal data, issues of less concern in traditional authentication systems. The irrevocability of biometric templates makes biometric systems very vulnerable to several attacks. In this paper we present a new approach for biometric template protection. Our objective is to build a non-invertible transformation, based on random projection, which meets the requirements of revocability, diversity, security and performance. In this context, we use the chaotic behavior of logistic map to build the projection vectors using a methodology that makes the construction of the projection matrix depend on the biometric template and its identity. The proposed approach has been evaluated and compared with Biohashing and BioPhasor using a rigorous security analysis. Our extensive experimental results using several databases (e.g., face, finger-knuckle and iris), show that the proposed technique has the ability to preserve and increase the performance of protected systems. Moreover, it is demonstrated that the security of the proposed approach is sufficiently robust to possible attacks keeping an acceptable balance between discrimination, diversity and non-invertibility.

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Mota, Mariana R. F., Pedro H. L. Silva, Eduardo J. S. Luz, Gladston J. P. Moreira, Thiago Schons, Lauro A. G. Moraes und David Menotti. „A deep descriptor for cross-tasking EEG-based recognition“. PeerJ Computer Science 7 (19.05.2021): e549. http://dx.doi.org/10.7717/peerj-cs.549.

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Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.

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Yoon, Seok-Joo, und Gwang-Jun Kim. „Personal Biometric Identification based on ECG Features“. Journal of the Korea institute of electronic communication sciences 10, Nr. 4 (30.04.2015): 521–26. http://dx.doi.org/10.13067/jkiecs.2015.10.4.521.

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DelPozo-Banos, Marcos, Carlos M. Travieso, Jesus B. Alonso und Ann John. „Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram“. International Journal of Neural Systems 28, Nr. 01 (20.12.2017): 1750035. http://dx.doi.org/10.1142/s0129065717500356.

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Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual’s brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology.

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Ibrahim, Yahya Ismail, und Israa Mohammed Alhamdani. „A hyprid technique for human footprint recognition“. International Journal of Electrical and Computer Engineering (IJECE) 9, Nr. 5 (01.10.2019): 4060. http://dx.doi.org/10.11591/ijece.v9i5.pp4060-4068.

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Biometrics has concerned a great care recently due to its important in the life that starts from civil applications to security and recently terrorism. A Footprint recognition is one of the personal identifications based on biometric measurements. The aim of this research is to design a proper and reliable biometric system for human footprint recognition named (FRBS) that stands for Footprint Recognition Biometric System. In addition, to construct a human footprint database which it is very helpful for various use in scientific application e.g. for authentication. There exist many biometrics databases for other identity but very rare for footprint. As well as the existing one are very limited. This paper presents a robust hyprid techniques which merges between Image Processing with Artificial Intelligent technique via Ant Colony Optimization (ACO) to recognize human footprint. (ACO) plays the essential role that rise the performance and the quality of the results in the biometric system via feature selection. The set of the selected features was treated as exploratory information, and selects the optimum feature set in standings of feature set size. Life RGB footprint images from nine persons with ten images per person constructed from life visual dataset. At first, the visual dataset was pre-processed operations. Each resultant image detects footprint that is cropped to portions represented by three blocks. The first block is for fingers, the second block refers to the center of the foot and the last one determines the heel. Then features were extracted from each image and stored in Excel file to be entered to Ant Colony Optimization Algorithm. The experimental outcomes of the system show that the proposed algorithm evaluates optimal results with smaller feature set comparing with other algorithms. Experimental outcomes show that our algorithm obtains an efficient and accurate result about 100% accuracy in comparison with other researches on the same field.

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Albakri, Ghazel, und Sharifa Alghowinem. „The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras“. Sensors 19, Nr. 8 (24.04.2019): 1928. http://dx.doi.org/10.3390/s19081928.

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Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.

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Nita, Stefania, Marius Mihailescu und Valentin Pau. „Security and Cryptographic Challenges for Authentication Based on Biometrics Data“. Cryptography 2, Nr. 4 (06.12.2018): 39. http://dx.doi.org/10.3390/cryptography2040039.

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Authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of the society, e.g., Smart City, Internet-of-Things (IoT), Cloud Computing, Big Data. In the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc. Due to its uniqueness, characteristic, biometrics systems will become more and more vulnerable, privacy being one of the most important challenges. The classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy. The current paper has several objectives. The main objective consists in creating a framework based on cryptographic modules which can be applied in systems with biometric authentication methods. The technologies used in creating the framework are: C#, Java, C++, Python, and Haskell. The wide range of technologies for developing the algorithms give the readers the possibility and not only, to choose the proper modules for their own research or business direction. The cryptographic modules contain algorithms based on machine learning and modern cryptographic algorithms: AES (Advanced Encryption System), SHA-256, RC4, RC5, RC6, MARS, BLOWFISH, TWOFISH, THREEFISH, RSA (Rivest-Shamir-Adleman), Elliptic Curve, and Diffie Hellman. As methods for implementing with success the cryptographic modules, we will propose a methodology which can be used as a how-to guide. The article will focus only on the first category, machine learning, and data clustering, algorithms with applicability in the cloud computing environment. For tests we have used a virtual machine (Virtual Box) with Apache Hadoop and a Biometric Analysis Tool. The weakness of the algorithms and methods implemented within the framework will be evaluated and presented in order for the reader to acknowledge the latest status of the security analysis and the vulnerabilities founded in the mentioned algorithms. Another important result of the authors consists in creating a scheme for biometric enrollment (in Results). The purpose of the scheme is to give a big overview on how to use it, step by step, in real life, and how to use the algorithms. In the end, as a conclusion, the current work paper gives a comprehensive background on the most important and challenging aspects on how to design and implement an authentication system based on biometrics characteristics.

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Bento, Nuno, David Belo und Hugo Gamboa. „ECG Biometrics Using Spectrograms and Deep Neural Networks“. International Journal of Machine Learning and Computing 10, Nr. 2 (Februar 2020): 259–64. http://dx.doi.org/10.18178/ijmlc.2020.10.2.929.

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Liu, Di, Ni Zhang und Kun Hu. „A Two Factor Authentication Algorithm for Medical Registration Platform“. Applied Mechanics and Materials 380-384 (August 2013): 1418–21. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1418.

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In order to protect user privacy information on the platform, this paper proposes a two factors authentication algorithm for medical registration platform. This algorithm make up gap that there is no authentication process during user log on the platform. This method is safer than traditional single factor user authentication methods, e.g., inputting password, or biometric authentication. For the proposed scheme, we figure out two different solutions, including SMS-OTP solution and biometrics based solution. Through comparisons at a different angle among these two solutions, a reasonable solution is chosen as the formal one to the platform.

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Karimian, Nima, Damon Woodard und Domenic Forte. „ECG Biometric: Spoofing and Countermeasures“. IEEE Transactions on Biometrics, Behavior, and Identity Science 2, Nr. 3 (Juli 2020): 257–70. http://dx.doi.org/10.1109/tbiom.2020.2992274.

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Shdefat, Ahmed Younes, Moon-Il Joo, Sung-Hoon Choi und Hee-Cheol Kim. „Utilizing ECG Waveform Features as New Biometric Authentication Method“. International Journal of Electrical and Computer Engineering (IJECE) 8, Nr. 2 (01.04.2018): 658. http://dx.doi.org/10.11591/ijece.v8i2.pp658-665.

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<p>In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.</p>

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Yadav, Nisha, Manoj Duhan und Anil Rose. „Biometric Human Recognition using ECG Signals“. IARJSET 4, Nr. 6 (20.06.2017): 168–71. http://dx.doi.org/10.17148/iarjset.2017.4630.

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Odinaka, Ikenna, Po-Hsiang Lai, Alan D. Kaplan, Joseph A. O'Sullivan, Erik J. Sirevaag und John W. Rohrbaugh. „ECG Biometric Recognition: A Comparative Analysis“. IEEE Transactions on Information Forensics and Security 7, Nr. 6 (Dezember 2012): 1812–24. http://dx.doi.org/10.1109/tifs.2012.2215324.

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37

Ingale, Mohit, Renato Cordeiro, Siddartha Thentu, Younghee Park und Nima Karimian. „ECG Biometric Authentication: A Comparative Analysis“. IEEE Access 8 (2020): 117853–66. http://dx.doi.org/10.1109/access.2020.3004464.

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Kadhim Abed, Jameel, und Nahrain N. Abd. „ECG Biometric Verification by using PCA“. International Journal of Engineering Trends and Technology 65, Nr. 1 (25.11.2018): 4–8. http://dx.doi.org/10.14445/22315381/ijett-v65p202.

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Maiorana, Emanuele, Jordi Solé-Casals und Patrizio Campisi. „EEG signal preprocessing for biometric recognition“. Machine Vision and Applications 27, Nr. 8 (23.08.2016): 1351–60. http://dx.doi.org/10.1007/s00138-016-0804-4.

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Yang, Su, Farzin Deravi und Sanaul Hoque. „Task sensitivity in EEG biometric recognition“. Pattern Analysis and Applications 21, Nr. 1 (29.07.2016): 105–17. http://dx.doi.org/10.1007/s10044-016-0569-4.

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41

Singh, Yogendra Narain. „Individual Identification Using Linear Projection of Heartbeat Features“. Applied Computational Intelligence and Soft Computing 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/602813.

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This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.

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Boubakeur, Meriem Romaissa, und Guoyin Wang. „Self-Relative Evaluation Framework for EEG-Based Biometric Systems“. Sensors 21, Nr. 6 (17.03.2021): 2097. http://dx.doi.org/10.3390/s21062097.

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In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.

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Donida Labati, Ruggero, Enrique Muñoz, Vincenzo Piuri, Roberto Sassi und Fabio Scotti. „Deep-ECG: Convolutional Neural Networks for ECG biometric recognition“. Pattern Recognition Letters 126 (September 2019): 78–85. http://dx.doi.org/10.1016/j.patrec.2018.03.028.

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BE, Manjunathswamy, Appaji M Abhishek, Thriveni J, Venugopal K R und L. M Patnaik. „Multimodal Biometric Authentication using ECG and Fingerprint“. International Journal of Computer Applications 111, Nr. 13 (18.02.2015): 33–39. http://dx.doi.org/10.5120/19601-1452.

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WU, Hung-Tsai, Yi-Ting WU und Wen-Whei CHANG. „Biometric Identification Using JPEG2000 Compressed ECG Signals“. IEICE Transactions on Information and Systems E98.D, Nr. 10 (2015): 1829–37. http://dx.doi.org/10.1587/transinf.2015edp7035.

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Astapov, A. A., D. V. Davydov, A. I. Egorov, D. V. Drozdov und E. M. Glukhovskij. „ECG-based biometric identification: some modern approaches“. Bulletin of Russian State Medical University, Nr. 1 (2016): 35–39. http://dx.doi.org/10.24075/brsmu.2016-01-06.

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Hammad, Mohamed, Gongning Luo und Kuanquan Wang. „Cancelable biometric authentication system based on ECG“. Multimedia Tools and Applications 78, Nr. 2 (03.07.2018): 1857–87. http://dx.doi.org/10.1007/s11042-018-6300-2.

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Maiorana, Emanuele. „Deep learning for EEG-based biometric recognition“. Neurocomputing 410 (Oktober 2020): 374–86. http://dx.doi.org/10.1016/j.neucom.2020.06.009.

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Maiorana, Emanuele, und Patrizio Campisi. „Longitudinal Evaluation of EEG-Based Biometric Recognition“. IEEE Transactions on Information Forensics and Security 13, Nr. 5 (Mai 2018): 1123–38. http://dx.doi.org/10.1109/tifs.2017.2778010.

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Rezgui, Dhouha, und Zied Lachiri. „ECG biometric recognition using SVM-based approach“. IEEJ Transactions on Electrical and Electronic Engineering 11 (Juni 2016): S94—S100. http://dx.doi.org/10.1002/tee.22241.

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