Journal articles: 'Multibiometric'

1

Jain, Anil K., and Arun Ross. "Multibiometric systems." Communications of the ACM 47, no. 1 (January 1, 2004): 34. http://dx.doi.org/10.1145/962081.962102.

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Ruchay, A. N. "DEVELOPMENT OF NEW ELECTIVE MULTIBIOMETRIC AUTHENTICATION." Journal of the Ural Federal District. Information security 20, no. 3 (2020): 34–41. http://dx.doi.org/10.14529/secur200304.

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The purpose of this work is the development of elective multibiometric authentication. The novelty of the work is to develop a new approach to multibiometric authentication. Depending on the availability and usability of sensors, from resistance to attacks, from diseases or injuries of users, any biometric characteristics can be selected, such as password rhythm, voice, dynam-ic signature, graphic password, etc. The paper presents the results of the development of elective multibiometric authentication based on a new approac

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Aftab, Anum, Farrukh Aslam Khan, Muhammad Khurram Khan, Haider Abbas, Waseem Iqbal, and Farhan Riaz. "Hand-based multibiometric systems: state-of-the-art and future challenges." PeerJ Computer Science 7 (October 7, 2021): e707. http://dx.doi.org/10.7717/peerj-cs.707.

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The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

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Selvarani, P., and N. Malarvizhi. "Multibiometric authentication with MATLAB simulation." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 47. http://dx.doi.org/10.14419/ijet.v7i1.7.9389.

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Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.

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Nair, Suresh Kumar Ramachandran, Bir Bhanu, Subir Ghosh, and Ninad S. Thakoor. "Predictive models for multibiometric systems." Pattern Recognition 47, no. 12 (December 2014): 3779–92. http://dx.doi.org/10.1016/j.patcog.2014.05.020.

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AlMahafzah, Harbi, and Maen Zaid AlRwashdeh. "A Survey of Multibiometric Systems." International Journal of Computer Applications 43, no. 15 (April 30, 2012): 36–43. http://dx.doi.org/10.5120/6182-8612.

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Kovaliuk, Tеtiana, Anastasiia Shevchenko, and Nataliia Kobets. "Multibiometric Identification of the Student by His Voice and Visual Biometric Indicators in the Process of Distance Education." Digital Platform: Information Technologies in Sociocultural Sphere 5, no. 1 (June 30, 2022): 90–102. http://dx.doi.org/10.31866/2617-796x.5.1.2022.261293.

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The purpose of the study is to reveal the essence of multibiometric identification of students and substantiate the feasibility of its use to improve quality and minimize errors in recognizing it using voice and visual biometric identifiers stored in audio files, video and photo images. Research Methodology. A systematic approach to determining the software requirements for a multibiometric human identification system, sound processing methods, neural network models as classifiers that identify a person by the vector of voice characteristics and methods of visual identification of a person by video stream and photo images were applied. Scientific Novelty. Methods for identifying the speaker’s voice signs, methods for identifying and registering a person by his voice signs, algorithms for visual identification of a person from her images in a video stream and from photo images based on the Viola-Jones, Eigenface and FisherFace algorithms have been further developed, and the architecture of a multibiometric identification system has been designed. Conclusions. Multibiometric identification of the student by voice and visual biometric indicators for the distance education system are offered. The system allows the extraction of acoustic characteristics from recording human language and further assignment of the obtained data to one of the predefined classes (speakers). A multilayer neural network (MNN) was used as a classifier. The classifier is trained on 43832 audio files from 108 speakers. MNN showed an accuracy of 91% in the test sample. The face in the frame is detected at the video stream frame processing stage, and the detected face is recognized. The system performed face recognition based on finding the most appropriate template of basic images stored in the database. A software system for recognizing and indexing people on video simultaneously with the identification of a person by voice has been developed, to use in the educational process for recording attendance at distance learning classes.

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Mahajan, Smita, and Asmita Deshpande. "Multibiometric Template Security using Fuzzy Vault." International Journal of Computer Applications 154, no. 3 (November 17, 2016): 21–26. http://dx.doi.org/10.5120/ijca2016912053.

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Gyaourova, Aglika, and Arun Ross. "Index Codes for Multibiometric Pattern Retrieval." IEEE Transactions on Information Forensics and Security 7, no. 2 (April 2012): 518–29. http://dx.doi.org/10.1109/tifs.2011.2172429.

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Kumar, Amioy, and Ajay Kumar. "A Cell-Array-Based Multibiometric Cryptosystem." IEEE Access 4 (2016): 15–25. http://dx.doi.org/10.1109/access.2015.2428277.

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Murakami, Takao, Tetsushi Ohki, and Kenta Takahashi. "Optimal sequential fusion for multibiometric cryptosystems." Information Fusion 32 (November 2016): 93–108. http://dx.doi.org/10.1016/j.inffus.2016.02.002.

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Li, Yong, Jian Ping Yin, and En Zhu. "An Evaluation Survey of Score Normalization in Multibiometric Systems." Advanced Engineering Forum 1 (September 2011): 168–72. http://dx.doi.org/10.4028/www.scientific.net/aef.1.168.

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Multibiometric fusion is an active research area for many years. Score normalization is to transform the scores from different matchers to a common domain. In this paper, we give a survey of classical score normalization techniques and recent advances of this research area. The performance of different normalization functions, such as MinMax, Tanh, Zscore, PL, LTL, RHE and FF are evaluated in XM2VTS Benchmark. We evaluated the performance with four different measures of biometric systems such as EER, AUC, GAR(FAR=0.001) and the threshold of EER. The experimental results show that there is no single normalization technique that would perform the best for all multibiometric recognition systems. PL and FF normalization outperform other methods in many applications.

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Roy, Kaushik, Brian O'Connor, Foysal Ahmad, and Mohamed S. Kamel. "Multibiometric System Using Level Set, Modified LBP and Random Forest." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450013. http://dx.doi.org/10.1142/s0219467814500132.

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Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibiometric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Second, a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of recognition performance, is applied. Third, to select the optimal subset of features from the fused feature vector, a feature subset selection scheme based on random forest (RF) is proposed. To evaluate the performance of the proposed scheme, the facial images of Yale Extended B Face database are fused with the iris images of CASIA V4 interval dataset to construct an iris–face multimodal biometric dataset. The experimental results indicate that the proposed multimodal biometrics system is more reliable and robust than the unimodal biometric scheme.

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Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, Noubeil Guermat, Lahcene Ziet, Mohamed Cheniti, and Khan Muhammad. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (March 10, 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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Peer, P., Ž. Emeršič, J. Bule, J. Žganec-Gros, and V. Štruc. "Strategies for Exploiting Independent Cloud Implementations of Biometric Experts in Multibiometric Scenarios." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/585139.

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Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.

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LI, Yong, Jian-Ping YIN, En ZHU, and Kuan LI. "Multibiometric Fusion Based on FAR and FRR." Acta Automatica Sinica 37, no. 4 (July 8, 2011): 408–17. http://dx.doi.org/10.3724/sp.j.1004.2011.00408.

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Bo Fu, S. X. Yang, Jianping Li, and Dekun Hu. "Multibiometric Cryptosystem: Model Structure and Performance Analysis." IEEE Transactions on Information Forensics and Security 4, no. 4 (December 2009): 867–82. http://dx.doi.org/10.1109/tifs.2009.2033227.

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Nagar, Abhishek, Karthik Nandakumar, and Anil K. Jain. "Multibiometric Cryptosystems Based on Feature-Level Fusion." IEEE Transactions on Information Forensics and Security 7, no. 1 (February 2012): 255–68. http://dx.doi.org/10.1109/tifs.2011.2166545.

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Pathak, Mrunal. "Multimodal Biometric Authentication for Smartphones." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1559–68. http://dx.doi.org/10.22214/ijraset.2021.39569.

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Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

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Salman, Duha D., Raghad A. Azeez, and Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm." Engineering and Technology Journal 38, no. 3B (December 25, 2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.

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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence. Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys. Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.

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NisarBhat, Asra, and Supreet Kaur. "Enhancement of Biometric Template Security in Multibiometric Systems." International Journal of Computer Applications 69, no. 10 (May 17, 2013): 36–41. http://dx.doi.org/10.5120/11882-7698.

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Peng, Jialiang, Qiong Li, Ahmed A. Abd El-Latif, and Xiamu Niu. "Finger multibiometric cryptosystems: fusion strategy and template security." Journal of Electronic Imaging 23, no. 2 (March 6, 2014): 023001. http://dx.doi.org/10.1117/1.jei.23.2.023001.

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Mahalakshmi, U. "An ECC Based Multibiometric System for Enhancing Security." Indian Journal of Science and Technology 6, no. 4 (April 20, 2013): 1–7. http://dx.doi.org/10.17485/ijst/2013/v6i4.8.

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Salman, Duha D., Raghad A. Azeez, and Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm." Engineering and Technology Journal 38, no. 3B (December 25, 2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.

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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence. Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys. Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.

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Garje, P. D. "Multibiometric Identification System Based On Score Level Fusion." IOSR Journal of Electronics and Communication Engineering 2, no. 6 (2012): 07–11. http://dx.doi.org/10.9790/2834-0260711.

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Peng, Jialiang, Qiong Li, Ahmed A. Abd El Latif, and Xiamu Niu. "Finger multibiometric cryptosystem based on score-level fusion." International Journal of Computer Applications in Technology 51, no. 2 (2015): 120. http://dx.doi.org/10.1504/ijcat.2015.068923.

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Modak, Sandip Kumar Singh, and Vijay Kumar Jha. "Multibiometric fusion strategy and its applications: A review." Information Fusion 49 (September 2019): 174–204. http://dx.doi.org/10.1016/j.inffus.2018.11.018.

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Thanki, Rohit M., and Komal R. Borisagar. "Experimental Study of Sparse Watermarking Techniques for Multibiometric System." Indian Journal of Science and Technology 8, no. 1 (January 16, 2015): 42. http://dx.doi.org/10.17485/ijst/2015/v8i1/52707.

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S. V. Patil, PrajaktaS Jadhav,. "Fused Index Code Based Multibiometric Pattern Retrieval Security System." International Journal of Innovative Research in Computer and Communication Engineering 4, no. 7 (July 30, 2016): 13019–26. http://dx.doi.org/10.15680/ijircce.2016.0407003.

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Zoubida, Leila, and Réda Adjoudj. "MultiBiometric Fusion: Left and Right Irises based Authentication Technique." International Journal of Image, Graphics and Signal Processing 9, no. 4 (April 8, 2017): 10–21. http://dx.doi.org/10.5815/ijigsp.2017.04.02.

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Islam, S. M. S., R. Davies, M. Bennamoun, R. A. Owens, and A. S. Mian. "Multibiometric human recognition using 3D ear and face features." Pattern Recognition 46, no. 3 (March 2013): 613–27. http://dx.doi.org/10.1016/j.patcog.2012.09.016.

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Canuto, Anne Magaly de Paula, Michael C. Fairhurst, and Fernando Pintro. "Ensemble systems and cancellable transformations for multibiometric‐based identification." IET Biometrics 3, no. 1 (March 2014): 29–40. http://dx.doi.org/10.1049/iet-bmt.2012.0032.

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Hussein, R. A., H. A. Jeiad, and M. N. Abdullah. "Multibiometric Identification System based on SVD and Wavelet Decomposition." Engineering and Technology Journal 35, no. 1Engineering (January 1, 2017): 61–67. http://dx.doi.org/10.30684/etj.2017.127311.

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Divya, R., and V. Vijayalakshmi. "Multibiometric Cryptosystem with Feature Level fusion Using Hard Fuzzy Logic." International Journal of Communication Technology for Social Networking Services 3, no. 2 (September 30, 2015): 55–62. http://dx.doi.org/10.21742/ijctsns.2015.3.2.05.

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Biggio, Battista, Giorgio Fumera, Gian Luca Marcialis, and Fabio Roli. "Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 3 (March 1, 2017): 561–75. http://dx.doi.org/10.1109/tpami.2016.2558154.

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Hariri, Mahdi. "Possibility of spoof attack against robustness of multibiometric authentication systems." Optical Engineering 50, no. 7 (July 1, 2011): 079001. http://dx.doi.org/10.1117/1.3599874.

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Babamir, Faezeh Sadat, and Mürvet Kırcı. "A multibiometric cryptosystem for user authentication in client-server networks." Computer Networks 181 (November 2020): 107427. http://dx.doi.org/10.1016/j.comnet.2020.107427.

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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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MURAKAMI, Takao, Yosuke KAGA, and Kenta TAKAHASHI. "Information-Theoretic Performance Evaluation of Multibiometric Fusion under Modality Selection Attacks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E99.A, no. 5 (2016): 929–42. http://dx.doi.org/10.1587/transfun.e99.a.929.

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Panneerselvam, Manonmani, R. Divya, and V. Vijayalakshmi. "Multilevel watermarking technique for securing multibiometric templates using DTCWT and SVD." International Journal of Image Mining 2, no. 3/4 (2017): 277. http://dx.doi.org/10.1504/ijim.2017.085321.

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Vijayalakshmi, V., R. Divya, and Manonmani Panneerselvam. "Multilevel watermarking technique for securing multibiometric templates using DTCWT and SVD." International Journal of Image Mining 2, no. 3/4 (2017): 277. http://dx.doi.org/10.1504/ijim.2017.10006323.

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Imran, Mohammad, Ashok Rao, and G. Hemantha Kumar. "Multibiometric systems: A comparative study of multi-algorithmic and multimodal approaches." Procedia Computer Science 2 (2010): 207–12. http://dx.doi.org/10.1016/j.procs.2010.11.026.

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Khodadoust, Javad, Ali Mohammad Khodadoust, Xiong Li, and Saru Kumari. "Design and implementation of a multibiometric system based on hand’s traits." Expert Systems with Applications 97 (May 2018): 303–14. http://dx.doi.org/10.1016/j.eswa.2017.12.035.

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Wu, Zhendong, Jiajia Yang, Jianwu Zhang, and Hengli Yue. "Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method." Security and Communication Networks 2018 (November 18, 2018): 1–12. http://dx.doi.org/10.1155/2018/5783976.

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Single biometric method has been widely used in the field of wireless multimedia authentication. However, it is vulnerable to spoofing and limited accuracy. To tackle this challenge, in this paper, we propose a multimodal fusion method for fingerprint and voiceprint by using a dynamic Bayesian method, which takes full advantage of the feature specificity extracted by a single biometrics project and authenticates users at the decision-making level. We demonstrate that this method can be extended to more modal biometric authentication and can achieve flexible accuracy of the authentication. The experiment of the method shows that the recognition rate and stability have been greatly improved, which achieves 4.46% and 5.94%, respectively, compared to the unimodal. Furthermore, it also increases 1.94% when compared with general multimodal methods for the biometric fusion recognition.

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Poornima, S., and S. Subramanian. "Experimental Analysis of Biometric System using Various Multimodal Fusion Algorithms." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2318/1/012037.

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Abstract Demand for high end privacy and security in human computer interaction, telecom environment is very high in the era of digital world. Multibiometric system combines information from multiple biometric traits of an individual and has an exceptional ability to address these demands with add-on customer satisfaction. It also overcomes intra class variations, non-universality, noisy data and attacks during authentication process. This paper proposes a multibiometric system suitable for secure access of data, devices and services. A database has been constructed using real time multiple biometric samples acquired from 500 individuals in an unconstrained environment. Existence of noise in the samples captured in an unconstrained environment are removed using filtering techniques, and the contrast is adjusted using dark channel priorities and scattering model. Then, the region of interest and features appropriate to each trait are extracted and fused in various forms like multiple samples, instances and traits in recognizing an individual. The proposed system is analysed by computing genuine and false acceptance rates. With the promising experimental results of various fusion schemes, the authentication is tested using transfer learning process with automatic extraction of essential features using Convolution Neural Network and classifying the target using Support Vector Machine (SVM), which outperforms in identifying an individual through fusion of biometric features acquired even in an unconstrained environment. Hence this authentication process could be modified into an effective one to identify and monitor the user interacting with a security related application in online mode with their unique available unconstrained features.

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Kıllıoğlu, Mehmet, Murat Taşkıran, and Nihan Kahraman. "Secure data transmission for multibiometric identity verification systems using steganography and encryption." Pamukkale University Journal of Engineering Sciences 24, no. 2 (2018): 173–79. http://dx.doi.org/10.5505/pajes.2016.39225.

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Therar, Huda Moyasar, Lect Dr Emad Ahmed Mohammed, and Asst Prof Dr Ahmed Jadaan Ali. "Multibiometric System for Iris Recognition Based Convolutional Neural Network and Transfer Learning." IOP Conference Series: Materials Science and Engineering 1105, no. 1 (June 1, 2021): 012032. http://dx.doi.org/10.1088/1757-899x/1105/1/012032.

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Gupta, Puneet, and Phalguni Gupta. "Multibiometric Authentication System Using Slap Fingerprints, Palm Dorsal Vein, and Hand Geometry." IEEE Transactions on Industrial Electronics 65, no. 12 (December 2018): 9777–84. http://dx.doi.org/10.1109/tie.2018.2823686.

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Nguyen, Kien, Simon Denman, Sridha Sridharan, and Clinton Fookes. "Score-Level Multibiometric Fusion Based on Dempster–Shafer Theory Incorporating Uncertainty Factors." IEEE Transactions on Human-Machine Systems 45, no. 1 (February 2015): 132–40. http://dx.doi.org/10.1109/thms.2014.2361437.

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Divya, R., and V. Vijayalakshmi. "Secure and efficient multibiometric fusion-based cryptosystem using blind separation encryption algorithm." International Journal of Biometrics 8, no. 2 (2016): 97. http://dx.doi.org/10.1504/ijbm.2016.077827.

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Journal articles on the topic 'Multibiometric'

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