Статті в журналах: "Voice biometry"

1

Lutsenko, K., and K. Nikulin. "VOICE SPEAKER IDENTIFICATION AS ONE OF THE CURRENT BIOMETRIC METHODS OF IDENTIFICATION OF A PERSON." Theory and Practice of Forensic Science and Criminalistics 19, no. 1 (April 2, 2020): 239–55. http://dx.doi.org/10.32353/khrife.1.2019.18.

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The article deals with the most widespread biometric identification systems of individuals, including voice recognition of the speaker on video and sound recordings. The urgency of the topic of identification of a person is due to the active informatization of modern society and the increase of flows of confidential information. The branches of the use of biometric technologies and their general characteristics are given. Here is an overview of the use of identification groups that characterize the voice. Also in the article the division of voice identification systems into the corresponding classes is given. The main advantages of voice biometrics such as simplicity of system realization are considered; low cost (the lowest among all biometric methods); No need for contact, the voice biometry allows for long-range verification, unlike other biometric technologies. The analysis of existing methods of speech recognition recognition identifying a person by a combination of unique voice characteristics, determining their weak and strong points, on the basis of which the choice of the most appropriate method for solving the problem of text-independent recognition, Namely the model of Gaussian mixtures, was carried out. The prerequisite for the development of speech technologies is a significant increase in computing capabilities, memory capacity with a significant reduction in the size of computer systems. It should also be Noted the development of mathematical methods that make it possible to perform the Necessary processing of an audio signal by isolating informative features from it. It has been established that the development of information technologies, and the set of practical applications, which use voice recognition technologies, make this area relevant for further theoretical and practical research.

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2

., 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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3

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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4

Adhinata, Faisal Dharma, Diovianto Putra Rakhmadani, and Alon Jala Tirta Segara. "Pengenalan Jenis Kelamin Manusia Berbasis Suara Menggunakan MFCC dan GMM." Journal of Dinda : Data Science, Information Technology, and Data Analytics 1, no. 1 (February 2, 2021): 28–33. http://dx.doi.org/10.20895/dinda.v1i1.198.

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Biometric information that exists in humans is unique from one human to another. One of the biometric data that is easily obtained is the human voice. The human voice is identic data that can differentiate between individuals. When we hear human voices directly, it is easy for our ears to tell the person who is speaking is male or female. But sometimes male voices can resemble girls and vice versa. Therefore, we propose a human voice detection system through Artificial Intelligence (AI) in machine learning. In this study, we used the Mel Frequency Cepstrum Coefficients (MFCC) method to extract human voice features and Gaussian Mixture Models (GMM) for the classification of female or male voice data. The experiment results showed that the system built was able to detect human gender through biometric voice data with an accuracy of 81.18%.

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5

Ouamour, Siham, and Halim Sayoud. "Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers." International Journal of Mobile Computing and Multimedia Communications 1, no. 4 (October 2009): 47–63. http://dx.doi.org/10.4018/jmcmc.2009072804.

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This article describes a new Speaker Discrimination System (SDS), which is a part of an overall project called Audio Documents Indexing based on a Speaker Discrimination System (ADISDS). Speaker discrimination consists in checking whether two speech segments come from the same speaker or not. This research domain presents an important field in biometry, since the voice remains an important feature used at distance (via telephone). However, although some discriminative classifiers do exist nowadays, their performances are not enough sufficient for short speech segments. This issue led us to propose an efficient fusion between such classifiers in order to enhance the discriminative performance. This fusion is obtained, by using three different techniques: a serial fusion, parallel fusion and serial-parallel fusion. Also, two classifiers have been chosen for the evaluation: a mono-gaussian statistical classifier and a Multi Layer Perceptron (MLP). Several experiments of speaker discrimination are conducted on different databases: Hub4 Broadcast-News and telephonic calls. Results show that the fusion has efficiently improved the scores obtained by each approach alone. So, for instance, we got an Equal Error Rate (EER) of about 7% on a subset of Hub4 Broadcast-News database, with short segments of 4 seconds, and an EER of about 4% on telephonic speech, with medium segments of 10 seconds.

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6

Singh, Nilu, Alka Agrawal, and R. A. Khan. "Voice Biometric: A Technology for Voice Based Authentication." Advanced Science, Engineering and Medicine 10, no. 7 (July 1, 2018): 754–59. http://dx.doi.org/10.1166/asem.2018.2219.

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7

Algabri, Mohammed, Hassan Mathkour, Mohamed A. Bencherif, Mansour Alsulaiman, and Mohamed A. Mekhtiche. "Automatic Speaker Recognition for Mobile Forensic Applications." Mobile Information Systems 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/6986391.

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Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.

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8

Abdalrahman, Roaya Salhalden A., Bülent Bolat, and Nihan Kahraman. "A cascaded voice biometric system." Procedia Computer Science 131 (2018): 1223–28. http://dx.doi.org/10.1016/j.procs.2018.04.334.

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9

Park, Hyun, and TaeGuen Kim. "User Authentication Method via Speaker Recognition and Speech Synthesis Detection." Security and Communication Networks 2022 (January 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/5755785.

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As the Internet has been developed, various online services such as social media services are introduced and widely used by many people. Traditionally, many online services utilize self-certification methods that are made using public certificates or resident registration numbers, but it is found that the existing methods pose the risk of recent personal information leakage accidents. The most popular authentication method to compensate for these problems is biometric authentication technology. The biometric authentication techniques are considered relatively safe from risks like personal information theft, forgery, etc. Among many biometric-based methods, we studied the speaker recognition method, which is considered suitable to be used as a user authentication method of the social media service usually accessed in the smartphone environment. In this paper, we first propose a speaker recognition-based authentication method that identifies and authenticates individual voice patterns, and we also present a synthesis speech detection method that is used to prevent a masquerading attack using synthetic voices.

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10

Mamyrbayev, Orken, Aizat Kydyrbekova, Keylan Alimhan, Dina Oralbekova, Bagashar Zhumazhanov, and Bulbul Nuranbayeva. "Development of security systems using DNN and i & x-vector classifiers." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (August 31, 2021): 32–45. http://dx.doi.org/10.15587/1729-4061.2021.239186.

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The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

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11

Gaubitch, Nikolay. "How voice ageing impacts biometric effectiveness." Biometric Technology Today 2017, no. 6 (June 2017): 8–9. http://dx.doi.org/10.1016/s0969-4765(17)30115-7.

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12

Shah, Hairol Nizam Mohd. "Biometric Voice Recognition in Security System." Indian Journal of Science and Technology 7, no. 1 (January 20, 2013): 104–12. http://dx.doi.org/10.17485/ijst/2014/v7i1.9.

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13

Harvianto, Harvianto, Livia Ashianti, Jupiter Jupiter, and Suhandi Junaedi. "Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method." ComTech: Computer, Mathematics and Engineering Applications 7, no. 2 (June 1, 2016): 131. http://dx.doi.org/10.21512/comtech.v7i2.2252.

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Voice recognition technology is one of biometric technology. Sound is a unique part of the human being which made an individual can be easily distinguished one from another. Voice can also provide information such as gender, emotion, and identity of the speaker. This research will record human voices that pronounce digits between 0 and 9 with and without noise. Features of this sound recording will be extracted using Mel Frequency Cepstral Coefficient (MFCC). Mean, standard deviation, max, min, and the combination of them will be used to construct the feature vectors. This feature vectors then will be classified using Support Vector Machine (SVM). There will be two classification models. The first one is based on the speaker and the other one based on the digits pronounced. The classification model then will be validated by performing 10-fold cross-validation.The best average accuracy from two classification model is 91.83%. This result achieved using Mean + Standard deviation + Min + Max as features.

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14

Gao, Yang, Yincheng Jin, Jagmohan Chauhan, Seokmin Choi, Jiyang Li, and Zhanpeng Jin. "Voice In Ear." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (March 19, 2021): 1–25. http://dx.doi.org/10.1145/3448113.

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With the rapid growth of wearable computing and increasing demand for mobile authentication scenarios, voiceprint-based authentication has become one of the prevalent technologies and has already presented tremendous potentials to the public. However, it is vulnerable to voice spoofing attacks (e.g., replay attacks and synthetic voice attacks). To address this threat, we propose a new biometric authentication approach, named EarPrint, which aims to extend voiceprint and build a hidden and secure user authentication scheme on earphones. EarPrint builds on the speaking-induced body sound transmission from the throat to the ear canal, i.e., different users will have different body sound conduction patterns on both sides of ears. As the first exploratory study, extensive experiments on 23 subjects show the EarPrint is robust against ambient noises and body motions. EarPrint achieves an Equal Error Rate (EER) of 3.64% with 75 seconds enrollment data. We also evaluate the resilience of EarPrint against replay attacks. A major contribution of EarPrint is that it leverages two-level uniqueness, including the body sound conduction from the throat to the ear canal and the body asymmetry between the left and the right ears, taking advantage of earphones' paring form-factor. Compared with other mobile and wearable biometric modalities, EarPrint is a low-cost, accurate, and secure authentication solution for earphone users.

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CUBIDES LOZANO, MARÍA FERNANDA, JUAN DAVID PRIETO RODRÍGUEZ, and VIOLETA SUAREZ HURTADO. "GENERACIÓN LLAVES DE CIFRADO A PARTIR DE PATRONES BIOMÉTRICOS HUMANOS (CASO ESTUDIO: FIRMA Y VOZ)." Revista Ingeniería, Matemáticas y Ciencias de la Información 5, no. 9 (January 15, 2018): 33–39. http://dx.doi.org/10.21017/rimci.2018.v5.n9.a38.

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16

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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17

Suman, M., K. Harish, K. Manoj Kumar, and S. Samrajyam. "Speech Recognition Using MFCC and VQLBG." International Journal of Advances in Applied Sciences 4, no. 4 (December 1, 2015): 151. http://dx.doi.org/10.11591/ijaas.v4.i4.pp151-156.

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Speaker Recognition is the computing task of confirmatory a user’s claimed identity mistreatment characteristics extracted from their voices. This technique is one of the most helpful and in style biometric recognition techniques in the world particularly connected to areas in that security could be a major concern. It are often used for authentication, police work, rhetorical speaker recognition and variety of connected activities. The method of Speaker recognition consists of two modules particularly feature extraction and have matching. Feature extraction is that the method during which we have a tendency to extract a tiny low quantity of knowledge from the voice signal that will later be used to represent every speaker. Feature matching involves identification of the unknown speaker by scrutiny the extracted options from his/her voice input with those from a collection of identified speakers. Our projected work consists of truncating a recorded voice signal, framing it, passing it through a window perform, conniving the Short Term FFT, extracting its options and Matching it with a hold on guide. Cepstral constant Calculation and Mel frequency Cepstral Coefficients (MFCC) area unit applied for feature extraction purpose.VQLBG (Vector Quantization via Linde-Buzo-Gray) algorithmic rule is used for generating guide and feature matching purpose.

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18

Et. al., ShashiRanjan. "Voice Biometric: A Novel and Realistic Approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 5684–94. http://dx.doi.org/10.17762/turcomat.v12i3.2243.

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Speaker identification uses the basic speaker's wave information to recognize the speaker. The device validates the speaker's identity, which makes the person eligible for the various services the voice can provide. This will fortify every device. Attributed voice is an algorithm focused on a speaker's physiological and behavioral characteristics. Speech analysis provides it with the distinguishing characteristics of identity, allowing the speaker to be distinguished from the others.

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19

Fong, Simon. "Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification." Journal of Biomedicine and Biotechnology 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/215019.

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Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers’ gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box) have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm.

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20

Jansen, Fieke, Javier Sánchez-Monedero, and Lina Dencik. "Biometric identity systems in law enforcement and the politics of (voice) recognition: The case of SiiP." Big Data & Society 8, no. 2 (July 2021): 205395172110636. http://dx.doi.org/10.1177/20539517211063604.

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Biometric identity systems are now a prominent feature of contemporary law enforcement, including in Europe. Often advanced on the premise of efficiency and accuracy, they have also been the subject of significant controversy. Much attention has focussed on longer-standing biometric data collection, such as finger-printing and facial recognition, foregrounding concerns with the impact such technologies can have on the nature of policing and fundamental human rights. Less researched is the growing use of voice recognition in law enforcement. This paper examines the case of the recent Speaker Identification Integrated Project, a European wide initiative to create the first international and interoperable database of voice biometrics, now the third largest biometric database at Interpol. Drawing on Freedom of Information requests, interviews and public documentation, we outline the emergence and features of SiiP and explore how voice is recognised and attributed meaning. We understand Speaker Identification Integrated Project as constituting a particular ‘regime of recognition’ premised on the use of soft biometrics (age, language, accent and gender) to disembed voice in order to optimise for difference. This, in turn, has implications for the nature and scope of law enforcement, people's position in society, and justice concerns more broadly.

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21

D S, Dr Dinesh Kumar. "Human Authentication using Face, Voice and Fingerprint Biometrics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 853–62. http://dx.doi.org/10.22214/ijraset.2021.36381.

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Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

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Danek, Paweł, Krzysztof Ćwirta, and Piotr Kopniak. "Extraction of parameters from biometric data samples." Journal of Computer Sciences Institute 13 (December 30, 2019): 323–31. http://dx.doi.org/10.35784/jcsi.1327.

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This article describes possible ways to extract parameters from biometric data samples, such as fingerprint or voice recording. Influence of particular approaches to biometric sample preparation and comparision algorithms accuracy was verified. Experiment involving processing big ammount of samples with usage of particular algorithms was performed. In fingerprint detection case the image normalization, Gabor filtering and comparision method based on descriptors were used. For voice authorization LPC and MFCC alghoritms were used. In both cases satisfying accuracy (60-80%) was the result of the surveys.

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Abd Aljabar, Raya W., and Nidaa F. Hassan. "Encryption VoIP based on Generated Biometric Key for RC4 Algorithm." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 209–21. http://dx.doi.org/10.30684/etj.v39i1b.1755.

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Voice over Internet Protocol (VoIP) calls are susceptible to interfere at many points by many attackers, thus encryption considered an important part in keeping VoIP. In this paper, Encryption VoIP based on Generated Biometric Key for RC4 Algorithm is proposed to encrypt the voice data before transmitting it over the network. The system uses a stream algorithm based on RC4 encryption with the new method of biometrics based Key generation technique. This system has generated complex keys in offline phase which is formed depend on features extracted using Linear Discernment Analysis (LDA) from face images. The experimental work shows that the proposed system offers secrecy to speech data with voice cipher is unintelligible and the recovered voice has perfect quality with MSR equal to zero and PSNR equal to infinity.

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Hedaia, Omar Ahmed, Ahmed Shawish, Essam H. Houssein, and Hala Zayed. "Bio-CAPTCHA Voice-Based Authentication Technique for Better Security and Usability in Cloud Computing." International Journal of Service Science, Management, Engineering, and Technology 11, no. 2 (April 2020): 59–79. http://dx.doi.org/10.4018/ijssmet.2020040104.

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Cloud computing has gained increased interest in the last few years, where an increasing number of providers are converging to such a promising platform. However, the security issues are still a big concern in the cloud, where authentication is a major one. Much research has been conducted to secure the authentication, where some of them used biometric features (fingerprint, face, and voice, etc.). In general, the biometric authentication techniques have a noticeable advantage compared to the traditional techniques because biometric features are hard to be altered or forged. Nevertheless, a new generation of attacks threatens the biometric security by using brute force approaches. This article proposes a nontraditional authentication technique that was called Bio-CAPTCHA. The proposed technique uses a random voice-based password challenge that dynamically changes every time the user tries to login, which promises to significantly decrease the possibility of unauthorized access. The conducted Experimental and theoretical analysis confirms the high-security level of the proposed technique.

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Duraibi, Salahaldeen, Frederick T. Sheldon, and Wasim Alhamdani. "Voice Biometric Identity Authentication Model for IoT Devices." International Journal of Security, Privacy and Trust Management 9, no. 2 (May 31, 2020): 1–10. http://dx.doi.org/10.5121/ijsptm.2020.9201.

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26

Abdulla, Waleed H., and Yushi Zhang. "Voice biometric feature using Gammatone filterbank and ICA." International Journal of Biometrics 2, no. 4 (2010): 330. http://dx.doi.org/10.1504/ijbm.2010.035448.

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27

Savchenko, V. V., and A. V. Savchenko. "Measurements method of the audio recordings acoustic quality indicator prepared for registration and processing in the Unified Biometric System." Izmeritel`naya Tekhnika, no. 12 (2019): 40–46. http://dx.doi.org/10.32446/0368-1025it.2019-12-40-46.

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We consider the task of automated quality control of sound recordings containing voice samples of individuals. It is shown that in this task the most acute is the small sample size. In order to overcome this problem, we propose the novel method of acoustic measurements based on relative stability of the pitch frequency within a voice sample of short duration. An example of its practical implementation using aninter-periodic accumulation of a speech signal is considered. An experimental study with specially developed software provides statistical estimates of the effectiveness of the proposed method in noisy environments. It is shown that this method rejects the audio recording as unsuitable for a voice biometric identification with a probability of 0,95 or more for a signal to noise ratio below 15 dB. The obtained results are intended for use in the development of new and modifying existing systems of collecting and automated quality control of biometric personal data. The article is intended for a wide range of specialists in the field of acoustic measurements and digital processing of speech signals, as well as for practitioners who organize the work of authorized organizations in preparing for registration samples of biometric personal data.

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El_Tokhy, Mohamed S. "Robust multimodal biometric authentication algorithms using fingerprint, iris and voice features fusion." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 647–72. http://dx.doi.org/10.3233/jifs-200425.

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Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications.

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Gomez-Alanis, Alejandro, Jose A. Gonzalez-Lopez, and Antonio M. Peinado. "GANBA: Generative Adversarial Network for Biometric Anti-Spoofing." Applied Sciences 12, no. 3 (January 29, 2022): 1454. http://dx.doi.org/10.3390/app12031454.

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Automatic speaker verification (ASV) is a voice biometric technology whose security might be compromised by spoofing attacks. To increase the robustness against spoofing attacks, presentation attack detection (PAD) or anti-spoofing systems for detecting replay, text-to-speech and voice conversion-based spoofing attacks are being developed. However, it was recently shown that adversarial spoofing attacks may seriously fool anti-spoofing systems. Moreover, the robustness of the whole biometric system (ASV + PAD) against this new type of attack is completely unexplored. In this work, a new generative adversarial network for biometric anti-spoofing (GANBA) is proposed. GANBA has a twofold basis: (1) it jointly employs the anti-spoofing and ASV losses to yield very damaging adversarial spoofing attacks, and (2) it trains the PAD as a discriminator in order to make them more robust against these types of adversarial attacks. The proposed system is able to generate adversarial spoofing attacks which can fool the complete voice biometric system. Then, the resulting PAD discriminators of the proposed GANBA can be used as a defense technique for detecting both original and adversarial spoofing attacks. The physical access (PA) and logical access (LA) scenarios of the ASVspoof 2019 database were employed to carry out the experiments. The experimental results show that the GANBA attacks are quite effective, outperforming other adversarial techniques when applied in white-box and black-box attack setups. In addition, the resulting PAD discriminators are more robust against both original and adversarial spoofing attacks.

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Savchenko, Vladimir V., and Andrey V. Savchenko. "Method of real-time updating for voice templates in the Unified Biometric System." Izmeritel`naya Tekhnika, no. 5 (2020): 58–65. http://dx.doi.org/10.32446/0368-1025it.2020-5-58-65.

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The article was written in the development of ideas from a previous work of the authors [5]. The task of automated quality control of voice templates, which were registered and stored in the Unified Biometric System is considered. A solution to the problem of timely updating of the collected templates is proposed, since over time they lose their consumer qualities. A new indicator of the acoustic quality of voice templates in the Kullback–Leibler information metric was investigated and a method for measuring it at the moments when users contacting the system with service requests was proposed. An example of the practical implementation of the proposed method is shown. Using the author's software, a full-scale experiment was conducted, quantitative estimates of the period for updating voice templates were obtained, and recommendations were given on their practical application. The results can be used to develop new and modernize existing systems and technologies for automated quality control and updating of biometric personal data templates.

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WAYMAN, JAMES L. "FUNDAMENTALS OF BIOMETRIC AUTHENTICATION TECHNOLOGIES." International Journal of Image and Graphics 01, no. 01 (January 2001): 93–113. http://dx.doi.org/10.1142/s0219467801000086.

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Biometric authentication technologies are used for the machine identification of individuals. The human-generated patterns used may be primarily physiological or behavioral, but usually contain elements of both components. Examples include voice, handwriting, face, eye and fingerprint identification. In this paper, we look at these technologies and their applications in general, developing a systematic approach to classifying, analyzing and evaluating them. A general system model is shown and test results for a number of technologies are considered.

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Ziyatbekova, Gulzat, Magzhan Aliaskar, Aisha Abjalilova, Diana Montaeva, and Arailym Turlybekova. "BIOMETRIC IDENTIFICATION OF A PERSON BY SEVERAL PARAMETERS." Herald of Kazakh-British technical university 18, no. 2 (June 1, 2021): 39–44. http://dx.doi.org/10.55452/1998-6688-2021-18-2-39-44.

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The article is devoted to the development of a system of biometric identification of a person by face,fingerprints and voice. Two-dimensional and three-dimensional characteristics of a person's face, taking into account area and volume, were used as informative signs of biometric identification of a person by face. A complex identification algorithm has been developed to account for such phenomena as portrait shift, different photo scales, and the tilt of the identified face. The FPM10A scanner and the Arduino microcontroller are used for biometric identification of a person by fingerprints. Identification signs are based on the analysis of the structure of papillary patterns on the finger: type and type of papillary pattern; direction and steepness of streams of papillary lines; the structure of the central pattern of the pattern; delta structure; the number of papillary lines between the center and the delta and many other signs. Another type of feature is local. They are also called minutiae (features or special points) — unique features inherent only in a particular print, determining the points of change in the structure of papillary lines (end, split, break, etc.), the orientation of papillary lines and coordinates at these points. Each print can contain up to 70 or more minutations. For biometric identification of a person by voice, MFC and PLP algorithms for digital processing and analysis of audio recordings are used. Various algorithms are used for acoustic speech analysis: hidden Markov models, a model of a mixture of Gaussian distributions. The result of determining the tone of speech and the content of speech for the purposes of voice identification is obtained. The Visual FoxPro DBMS has developed a «multiparametric automated system for biometric identification of an individual».

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M. B, Sanjaypande, and Raikoti Sharanabasappa. "A Unique Wavelet Steganography Based Voice Biometric Protection Scheme." International Journal of Image, Graphics and Signal Processing 5, no. 3 (March 3, 2013): 42–47. http://dx.doi.org/10.5815/ijigsp.2013.03.06.

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Bouaﬁf, Lamia, and Noureddine Ellouze. "Implementation of a Biometric Interface in Voice Controlled Wheelchairs." Sound&Vibration 54, no. 1 (2020): 1–15. http://dx.doi.org/10.32604/sv.2020.08665.

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Tereikovska, Liudmyla. "METHOD OF NEURAL NETWORK ANALYSIS OF VOICE SIGNAL." Cybersecurity: Education, Science, Technique 3, no. 7 (2020): 31–42. http://dx.doi.org/10.28925/2663-4023.2020.7.3142.

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The article is devoted to increase of efficiency of the means of analysis of biometric characteristics of subjects, interacting with information-control systems of various purpose. It is shown that from the standpoint of the possibility of using as a sensor the reading of the biometric parameters of the standard peripheral equipment of the computer, the widespread use in the information-control systems of voice messages, their high informativeness, the complexity of falsification of biometric information, as well as the possibility of carrying out hidden monitoring, the prospects have analysis tools voice signal. The necessity of improvement of methodology of neural network analysis of voice signal for recognition of emotions and person is grounded. Possibility of increase of efficiency of neural network means of analysis due to adaptation of parameters of neural network model to the conditions of use is determined. The principles of determination of valid neural network models and the most effective type of neural network model used for voice signal analysis have been formed. A coding procedure for the input signal is developed, which allows to use in the neural network a compact representation of the most informative features of a voice signal. A procedure for encoding a neural network output signal has also been developed to increase the efficiency of its learning. The method of neural network analysis of the voice signal is developed, which due to the offered principles of adaptation and procedures of coding of input and output parameters, allows to introduce into neural means a neural network whose architecture is adapted to the expected conditions of use. The effectiveness of the proposed method has been proven experimentally. Experimental studies have shown that the use of the developed method allows to ensure the accuracy of recognition of emotions of the identified speaker, which is approximately 0.94, which corresponds to the best modern decisions in this field. It is suggested to correlate the ways of further research with the development of solutions that would allow to analyze the voice signal of arbitrary duration under the conditions of noise of different kind.

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Ihsan, Indah Purwitasari, Sukriyah Buwarda, Hilda Novianty, and Ifsan Aditya Putra. "Voice Recognition Untuk Otomatisasi Sistem Pengakses Pintu." JSAI (Journal Scientific and Applied Informatics) 4, no. 1 (January 31, 2021): 116–25. http://dx.doi.org/10.36085/jsai.v4i1.1318.

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Penggunaan kunci manual sebagai pembuka dan pengunci pintu masih belum optimal. Masalah yang sering terjadi adalah pemilik kunci sering kali lupa dimana menyimpan kunci bahkan hingga terjadi kehilangan kunci. Sistem biometrik pola suara memiliki ciri khas dan karakteristik yang berbeda pada setiap manusia, maka suara dapat dijadikan salah satu alternatif solusi, yaitu suara sebagai kunci untuk membuka pintu secara otomatis sehingga lebih efisien. Otomatisasi sistem pengunci pintu dibuat menggunakan Elechouse v3 yang berfungsi sebagai voice recognition serta Solenoid lock door sebagai pengunci otomatis pada pintu. Hasil pengujian fungsional menggunakan black box menunjukkan bahwa seluruh alat yang dirangkai berfungsi sesuai fungsinya. Pengujian tingkat keberhasilan sistem dilakukan menggunakan variable derau, non derau dan jarak. Pada data training tingkat keberhasilan sistem ini jika tanpa derau adalah 100%, sedangkan dengan derau 50.0 dB hingga 70 dB rata-rata tingkat keberhasilannya adalah 56,2%. Untuk jarak 30 cm sampai 180 cm rata-rata keberhasilannya sebesar 40,51%. Jarak terjauh adalah pada jarak 150 cm dengan presentase keberhasilan 5%. Pada data testing tingkat keberhasilannya jika tanpa derau adalah 0%, sedangkan dengan derau 50.0 dB hingga 70 dB rata-rata tingkat keberhasilannya adalah 1,9%. Untuk jarak 30 cm sampai 180 cm rata-rata keberhasilannya sebesar 0%.The use of manual locks as door openers and locks is still not optimal. The problem that often occurs is that the key owner often forgets where to store the key and even loses the key. The voice pattern biometric system has different characteristics for each human, so sound can be an alternative solution, namely voice as a key to open doors automatically so that it is more efficient. Door lock system automation is made using Elechouse v3 which functions as voice recognition and Solenoid door lock as automatic locking of doors. The results of functional testing using a black box show that all the tools assembled function according to their function. Testing the success rate of the system is carried out using noise, non-noise and distance variables. In the training data, the success rate of this system without noise is 100%, while with a noise of 50.0 dB to 70 dB the average success rate is 56.2%. For a distance of 30 cm to 180 cm the success rate is 40.51%. The farthest distance is at a distance of 150 cm with a success percentage of 5%. In the testing data, the success rate without noise is 0%, while with a noise of 50.0 dB to 70 dB the average success rate is 1.9%. For a distance of 30 cm to 180 cm the success rate is 0%.

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Bakir, Cigdem. "Speech recognition system for Turkish language with hybrid method." Global Journal of Computer Sciences: Theory and Research 7, no. 1 (November 27, 2017): 48–57. http://dx.doi.org/10.18844/gjcs.v7i1.2699.

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Currently, technological developments are accompanied by a number of associated problems. Security takes the first place amongst such problems. In particular, biometric systems such as authentication constitute a significant fraction of the security problem. This is because sound recordings having connection with various crimes are required to be analysed for forensic purposes. Authentication systems necessitate transmission, design and classification of biometric data in a secure manner. The aim of this study is to actualise an automatic voice and speech recognition system using wavelet transform, taking Turkish sound forms and properties into consideration. Approximately 3740 Turkish voice samples of words and clauses of differing lengths were collected from 25 males and 25 females. The features of these voice samples were obtained using Mel-frequency cepstral coefficients (MFCCs), Mel-frequency discrete wavelet coefficients (MFDWCs) and linear prediction cepstral coefficient (LPCC). Feature vectors of the voice samples obtained were trained with k-means, artificial neural network (ANN) and hybrid model. The hybrid model was formed by combining with k-means clustering and ANN. In the first phase of this model, k-means performed subsets obtained with voice feature vectors. In the second phase, a set of training and tests were formed from these sub-clusters. Thus, for being trained more suitable data by clustering increased the accuracy. In the test phase, the owner of a given voice sample was identified by taking the trained voice samples into consideration. The results and performance of the algorithms used for classification are also demonstrated in a comparative manner. Keywords: Speech recognition, hybrid model, k-means, artificial neural network (ANN).

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Paś, Jacek. "Diagnostic station for a multibiometric system for a selected transport object." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 12 (December 31, 2018): 585–88. http://dx.doi.org/10.24136/atest.2018.457.

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Multibiometric systems used in transport objects, in contradistinction to "ordinary" biometric systems, use several recognition techniques, e.g. fingerprint, iris, voice or face. Biometric devices are sometimes part of electronic security systems. These systems are currently installed in many transport facilities - stationary and non-stationary where there is a lot of personal traffic. These devices are most often used in extensive areas, airports, logistics bases or railway stations. The article presents issues concerning the diagnostic position for a biometric system which has in its structure several simple identification techniques.

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Hadi, Iman H., and Alia K. Abdul-Hassan. "A Proposed Speaker Recognition Method B Based on Long-Term Voice Features and Fuzzy Logic." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 1–10. http://dx.doi.org/10.30684/etj.v39i1b.343.

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Speaker recognition depends on specific predefined steps. The most important steps are feature extraction and features matching. In addition, the category of the speaker voice features has an impact on the recognition process. The proposed speaker recognition makes use of biometric (voice) attributes to recognize the identity of the speaker. The long-term features were used such that maximum frequency, pitch and zero crossing rate (ZCR). In features matching step, the fuzzy inner product was used between feature vectors to compute the matching value between a claimed speaker voice utterance and test voice utterances. The experiments implemented using (ELSDSR) data set. These experiments showed that the recognition accuracy is 100% when using text dependent speaker recognition.

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Kim, Tae-Yeun, Libor Měsíček, and Sung-Hwan Kim. "Modeling of Child Stress-State Identification Based on Biometric Information in Mobile Environment." Mobile Information Systems 2021 (April 14, 2021): 1–13. http://dx.doi.org/10.1155/2021/5531770.

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A technology must be developed to automatically identify extreme stress states of children who cannot properly express their emotions when recognizing dangerous situations, which threaten the safety of children, in real time. This study presents a stress-state identification model for children based on machine learning, biometric data, a smart band for collecting biometric data, and a mobile application for monitoring the stress state of the child classified. In addition, through an experiment comparing a dataset using only voice data and a dataset using both voice and heart rate data, we aimed to verify the effectiveness of the combination of the two biosignal datasets. As a result of the experiment, the SVM model showed the highest performance with an accuracy of 88.53% for the dataset using both voice data and heart rate data. The results of this study presented strong implications for the possibility of automating the stress-state identification of a child, and it is expected that the developed method can be used to take preventive measures for dangerous situations to children.

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Krämer, Nicole C., Gary Bente, Felix Eschenburg, and Heide Troitzsch. "Embodied Conversational Agents." Social Psychology 40, no. 1 (January 2009): 26–36. http://dx.doi.org/10.1027/1864-9335.40.1.26.

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It was analyzed whether an embodied conversational agent (ECA) has specific advantages when employed with privacy invading technologies such as a biometric security system. The study compares the effects of an ECA interface with the effects of conventional text-based and voice-based interfaces on user acceptance and usability. An additional variable was whether the biometric system falsely rejected the user twice or whether it directly accepted him/her. Results of the 2 × 3 between-subjects design indicated that, although overall the text interface is rated most positive, voice and ECA yield distinct social effects: They have more advantageous consequences when problems arise – i.e., when the user is rejected repeatedly. The implications for social psychology in terms of applicability of new research methods as well as insights concerning fundamental research are discussed.

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42

Gorai, Kumari Piu, and Thomas Abraham. "A GAUSSIAN MIXTURE MODEL-BASED SPEAKER RECOGNITION SYSTEM." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 140. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19596.

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A human being has lot of unique features and one of them is voice. Speaker recognition is the use of a system to distinguish and identify a person from his/her vocal sound. A speaker recognition system (SRS) can be used as one of the authentication technique, in addition to the conventional authentication methods. This paper represents the overview of voice signal characteristics and speaker recognition techniques. It also discusses the advantages and problem of current SRS. The only biometric system that allows users to authenticate remotely is voice-based SRS, we are in the need of a robust SRS.

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Bhirud, Priya, and Nandana Prabhu. "Performance Evaluation of Filters of Discrete Wavelet Transforms for Biometrics." International Journal of Informatics and Communication Technology (IJ-ICT) 3, no. 2 (August 1, 2014): 97. http://dx.doi.org/10.11591/ijict.v3i2.pp97-102.

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Biometrics associated with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Commonly used biometric features are facial features, fingerprints, voice, facial thermo grams, iris, posture/gait, palm print, hand geometry etc. Compared with other biometric characteristics iris is the most stable and hence the most reliable biometric characteristic over the period of a lifetime. This proposed work provides comparative study of various filters of Wavelet Transforms in terms of size and PSNR of images.

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Danish, Aafreen, Khushali Hedau, Diksha Ukey, Anisha Walde, Uzma Sohail Sheikh, and Prof Akbar Nagani. "Fingerprint,Face and Voice Recognition Based Attendance Monitoring System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 749–52. http://dx.doi.org/10.22214/ijraset.2022.41346.

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Abstract: In This project aims to record the attendance without manual intervention. In the area of technology that changes and modifies daily, use biometrics is the most popular and trending technology. Taking attendance manually for a class of almost 60-80 students can be a time-consuming task if thought of it in a long run Each person has a unique biometric feature such as fingerprint, face structure, voice detection etc. Keywords— Boimetrics, Fringerprint, Face Structure,Voice Detection.

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45

D.S., Dinesh Kumar, and P. V. Rao. "Implementing and analysing FAR and FRR for face and voice recognition (multimodal) using KNN classifier." International Journal of Intelligent Unmanned Systems 8, no. 1 (October 4, 2019): 55–67. http://dx.doi.org/10.1108/ijius-02-2019-0015.

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Purpose The purpose of this paper is to incorporate a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Biometrics plays a major role in several areas including military applications because of robustness of the system. Speech and face data are considered as key elements that are commonly used for multimodal biometric applications, as they are simultaneously acquired from camera and microphone. Design/methodology/approach In this proposed work, Viola‒Jones algorithm is used for face detection, and Local Binary Pattern consists of texture operators that perform thresholding operation to extract the features of face. Mel-frequency cepstral coefficients exploit the performances of voice data, and median filter is used for removing noise. KNN classifier is used for fusion of both face and voice. The proposed method produces better results in noisy environment with better accuracy. In this proposed method, from the database, 120 face and voice samples are trained and tested with simulation results using MATLAB tool that improves performance in better recognition and accuracy. Findings The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent. Originality/value The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

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Mattoo, Iqra, and Parul Agarwal. "Iris Biometric Modality: A Review." Oriental journal of computer science and technology 10, no. 2 (May 20, 2017): 502–6. http://dx.doi.org/10.13005/ojcst/10.02.35.

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Biometric Recognition is the most suitable and informed identification method which is used in different fields due to its uniqueness of the countless behavioural and physiological traits like hand geometry, finger prints, iris recognition, face recognition, handwriting, voice recognition, etc. Iris recognition system is widely being used as it has inherently distinctive patterns that provide a robust method for the identification purpose. Different nations have already started to use biometric recognition system for the identification purposes including patient identification, border security, etc. In this review paper, different steps that are involved in Iris Recognition system are defined and evaluation of different Iris Recognition methods used by different researchers for each recognition step is done as well.

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Vittori, Piergiorgio. "Ultimate password: is voice the best biometric to beat hackers?" Biometric Technology Today 2019, no. 9 (October 2019): 8–10. http://dx.doi.org/10.1016/s0969-4765(19)30127-4.

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48

Winda, A., W. R. E. Byan, Sofyan, Armansyah, D. L. Zariantin, and B. G. Josep. "Motorcycle Start-stop System based on Intelligent Biometric Voice Recognition." IOP Conference Series: Materials Science and Engineering 187 (March 2017): 012039. http://dx.doi.org/10.1088/1757-899x/187/1/012039.

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Belova, E. P., and I. V. Mashkina. "Architecture and Operation Algorithm of a Biometric Voice Authentication System." Automatic Control and Computer Sciences 55, no. 8 (December 2021): 924–31. http://dx.doi.org/10.3103/s014641162108006x.

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Shawkat1, Shihab A., and Raya N. Ismail2. "Biometric Technologies in Recognition Systems: A Survey." Tikrit Journal of Pure Science 24, no. 6 (November 3, 2019): 132. http://dx.doi.org/10.25130/j.v24i6.899.

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The ability to recognize people uniquely and to associate personal attributes such as name and nationality with them has been very important to the fabric of human society. Nowadays, modern societies have an explosion in population growth and increased mobility which necessitated building advanced identity management systems for recording and maintaining people’s identities. In the last decades, biometrics has played an important role in recognizing people instead of traditional ways such as passwords and keys which can be forgotten or be stolen. Biometric systems employ physiological and/or behavioral characteristics of people to verify their identities. There are different biometric modalities that can be used to recognize people such as fingerprints, face, hand geometry, voice, iris, signature, etc. In this paper, a comprehensive overview have been provided on the major issues of biometric systems including general biometric system architecture, major biometric traits, biometric systems performance, and some relevant works. http://dx.doi.org/10.25130/tjps.24.2019.120

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