Supervised and Unsupervised Learning Techniques for Biometric Systems

生物识别 计算机科学 人工智能 无监督学习 机器学习 模式识别(心理学)
作者
Pallavi Pandey,Yogita Yashveer Raghav,Sarita Gulia,Sagar Aggarwal,Nitin Kumar
标识
DOI:10.1002/9781119786443.ch12
摘要

Chapter 12 Supervised and Unsupervised Learning Techniques for Biometric Systems Pallavi Pandey, Pallavi Pandey Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorYogita Yashveer Raghav, Yogita Yashveer Raghav Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorSarita Gulia, Sarita Gulia Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorSagar Aggarwal, Sagar Aggarwal Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorNitin Kumar, Nitin Kumar Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this author Pallavi Pandey, Pallavi Pandey Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorYogita Yashveer Raghav, Yogita Yashveer Raghav Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorSarita Gulia, Sarita Gulia Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorSagar Aggarwal, Sagar Aggarwal Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this authorNitin Kumar, Nitin Kumar Department of Computer Science and Engineering, School of Engineering and Technology, KR Mangalam University, Gurugram, IndiaSearch for more papers by this author Book Editor(s):Suman Kumar Swarnkar, Suman Kumar SwarnkarSearch for more papers by this authorJ P Patra, J P PatraSearch for more papers by this authorSapna Singh Kshatri, Sapna Singh KshatriSearch for more papers by this authorYogesh Kumar Rathore, Yogesh Kumar RathoreSearch for more papers by this authorTien Anh Tran, Tien Anh TranSearch for more papers by this author First published: 01 April 2024 https://doi.org/10.1002/9781119786443.ch12 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary In the domain of biometric systems, where individual identities are confirmed through distinctive traits like fingerprints, facial geometry, and gait patterns, the application of supervised and unsupervised machine learning techniques plays a pivotal role. This chapter provides a comprehensive examination of these approaches within the context of biometric systems. Supervised learning, which relies on labeled data to train models for predicting outcomes, has proven effective in various biometric applications, employing algorithms such as Convolutional Neural Networks, Support Vector Machines, logistic regression, and Decision trees. Unsupervised learning, in contrast, excels in automatic feature extraction, data analysis, and learning strategy creation. While it may not be the primary choice for identification, it contributes significantly to improved feature fusion and data analysis. This chapter offers a detailed exploration of these machine learning techniques, assessing their suitability for both identification and verification processes. Furthermore, it addresses the persistent challenges faced in biometric system development, ranging from handling numerous identities and security concerns to extracting relevant data from noisy inputs. Privacy, data breaches, and the evolving nature of biometric attributes are also discussed. With biometrics increasingly integrated into everyday devices like smartphones, this chapter underscores the balance required between security and usability, exploring the motivations driving enhancements in biometric recognition methods to meet the growing demands for performance, usability, and security. Additionally, the chapter provides a comprehensive overview of various biometric techniques, highlighting their respective advantages and challenges, thereby offering insights into their uniqueness and application suitability. In summary, this chapter serves as an invaluable resource for those involved in the dynamic and ever-evolving field of biometrics. References Sundararajan , K. , & Woodard , D. L. ( 2018 ). Deep learning for biometrics: A survey . ACM Computing Surveys (CSUR) , 51 ( 3 ), 1 - 34 . 10.1145/3190618 Web of Science®Google Scholar Nigam , A. , & Gupta , P. ( 2015 ). Designing an accurate hand biometric based authentication system fusing finger knuckleprint and palmprint . Neurocomputing , 151 , 1120 - 1132 . 10.1016/j.neucom.2014.03.083 Web of Science®Google Scholar Khatun , F. , Distler , R. , Rahman , M. , O'Donnell , B. , Gachuhi , N. , Alwani , M. , … & Friberg , I. K. ( 2022 ). Comparison of a palm-based biometric solution with a name-based identification system in rural Bangladesh . Global Health Action , 15 ( 1 ), 2045769 . 10.1080/16549716.2022.2045769 PubMedGoogle Scholar Gawande , U. , Golhar , Y. , & Hajari , K. ( 2017 ). Biometric-based security system: Issues and challenges . Intelligent Techniques in Signal Processing for Multimedia Security , 151 - 176 . 10.1007/978-3-319-44790-2_8 Google Scholar Kataria , A. N. , Adhyaru , D. M. , Sharma , A. K. , & Zaveri , T. H. ( 2013 , November). A survey of automated biometric authentication techniques . In 2013 Nirma university international conference on engineering (NUiCONE) (pp. 1 - 6 ). IEEE . 10.1109/NUiCONE.2013.6780190 Google Scholar Saini , R. , & Rana , N. ( 2014 ). Comparison of various biometric methods . International Journal of Advances in Science and Technology , 2 ( 1 ), 24 - 30 . Google Scholar Burgan , D. A. , & Baker , L. A. ( 2009 ). Investigating Self-Assembly with Macaroni . Journal of Chemical Education , 86 ( 6 ), 704A . 10.1021/ed086p704A CASGoogle Scholar Bouridane , A. ( 2009 ). Introduction and Preliminaries on Biometrics and Forensics Systems . In Imaging for Forensics and Security (pp. 1 - 10 ). Springer , Boston, MA . 10.1007/978-0-387-09532-5_1 Google Scholar Sharif , M. , Raza , M. , Shah , J. H. , Yasmin , M. , & Fernandes , S. L. ( 2019 ). An overview of biometrics methods . Handbook of Multimedia Information Security: Techniques and Applications , 15 - 35 . 10.1007/978-3-030-15887-3_2 Google Scholar Galbally , J. , Fierrez , J. , Ortega-Garcia , J. ( 2007 ). Vulnerabilities in biometric systems: Attacks and recent advances in liveness detection . Database , 1 ( 3 ), 4 . Google Scholar Jain , A. K. , Nandakumar , K. , Nagar , A. ( 2008 ). Biometric template security . EURASIP Journal on Advances in Signal Processing , 2008 , 113 . 10.1155/2008/579416 Web of Science®Google Scholar H. Kaur , P. Khanna , Gaussian Random Projection Based Non-invertible Cancelable Biometric Templates , Procedia Computer Science , vol. 54 , pp. 661 – 670 , 2015 . 10.1016/j.procs.2015.06.077 Google Scholar M.A. Dabbah , W.L. Woo , S.S. Dlay , Secure authentication for face recognition , in IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007 . hskip 1em plus 0.5em minus 0.4em2007, pp. 121 – 126 . Google Scholar M.A. Dabbah , W.L. Woo , S.S. Dlay , Appearance-Based Biometric Recognition: Secure Authentication and Cancellability , in IEEE 15th International Conference on Digital Signal Processing . hskip 1em plus 0.5em minus 0.4em2007, pp. 479 – 482 . Google Scholar P. Li , X. Yang , H. Qiao , K. Cao , E. Liu , J. Tian , An effective biometric cryptosystem combining fingerprints with error correction codes , Expert Systems with Applications , vol. 39 , no. 7 , pp. 6562 – 6574 , 2012 . 10.1016/j.eswa.2011.12.048 Web of Science®Google Scholar Dong , X. , Jin , Z. , Zhao , L. , Guo , Z. , ( 2021 ). BioCanCrypto: An LDPC coded bio-cryptosystem on fingerprint cancellable template , in: 2021 IEEE International Joint Conference on Biometrics (IJCB) , Shenzhen, China , pp. 1 - 8 . 10.1109/IJCB52358.2021.9484391 Google Scholar Y. Imamverdiyev , A.B.J. Teoh , J. Kim , Biometric cryptosystem based on discretized fingerprint texture descriptors , Expert Systems with Applications , vol. 40 , no. 5 , pp. 1888 – 1901 , 2013 . 10.1016/j.eswa.2012.10.009 Web of Science®Google Scholar C. Rathgeb , C. Busch , Cancelable multi-biometrics: Mixing iris-codes based on adaptive bloom filters , Computers & Security , vol. 42 , pp. 1 – 12 , 2014 . 10.1016/j.cose.2013.12.005 Web of Science®Google Scholar Gragnaniello , D. , Sansone , C. , Verdoliva , L. ( 2015 ). Iris liveness detection for mobile devices based on local descriptors . Pattern Recognition Letters , 57 , 81 - 87 . 10.1016/j.patrec.2014.10.018 Google Scholar Mondal , S. , Bours , P. ( 2015 . A computational approach to the continuous authentication biometric system . Information Sciences , 304 , 28 - 53 . 10.1016/j.ins.2014.12.045 Google Scholar Chakraborty , S. , Balasubramanian , V. , Panchanathan , S. ( 2013 ). Generalized batch mode active learning for face-based biometric recognition . Pattern Recognition , 46 ( 2 ), 497 - 508 . 10.1016/j.patcog.2012.07.025 Web of Science®Google Scholar Bharadwaj , S. , Bhatt , H. S. , Singh , R. , Vatsa , M. , Noore , A. ( 2015 ). QFuse: Online learning framework for adaptive biometric system . Pattern Recognition , 48 ( 11 ), 3428 - 3439 . 10.1016/j.patcog.2015.05.002 Google Scholar Arigbabu , O. A. , Ahmad , S. M. S. , Adnan , W. A. W. , Yussof , S. ( 2015 ). Integration of multiple soft biometrics for human identification . Pattern Recognition Letters , 68 , 278 - 287 . 10.1016/j.patrec.2015.07.014 Web of Science®Google Scholar De Marsico , M. , Petrosino , A. , Ricciardi , S. ( 2016 ). Iris Recognition through Machine Learning Techniques: a Survey . Pattern Recognition Letters. 10.1016/j.patrec.2016.02.001 Google Scholar Nanni , L. , Lumini , A. , Ferrara , M. , Cappelli , R. ( 2015 ). Combining biometric matchers by means of machine learning and statistical approaches . Neurocomputing , 149 , 526 - 535 . 10.1016/j.neucom.2014.08.021 Google Scholar Kim , D. J. , Shin , J. H. , Hong , K. S. ( 2010 ). Teeth recognition based on multiple attempts in mobile device . Journal of Network and Computer Applications , 33 ( 3 ), 283 - 292 . 10.1016/j.jnca.2009.12.016 Google Scholar Bouadjenek , N. , Nemmour , H. , Chibani , Y. ( 2015 ). Robust softbiometrics prediction from off-line handwriting analysis . Applied Soft Computing. PubMedGoogle Scholar Kim , D. J. , Chung , K. W. , Hong , K. S. ( 2010 ). Person authentication using face, teeth and voice modalities for mobile device security . IEEE . 10.1109/TCE.2010.5681156 Google Scholar Transactions on Consumer Electronics , 56 ( 4 ), 2678 - 2685 . Google Scholar [31] Hazen , T. J. , Weinstein , E. , Park , A. ( 2003 , November). Towards robust person recognition on handheld devices using face and speaker identification technologies . In Proceedings of the 5th International Conference on Multimodal Interfaces (pp. 289 - 292 ). ACM . 10.1145/958432.958485 Google Scholar Tresadern , P. A. , McCool , C. , Poh , N. , Matejka , P. , Hadid , A. , Levy , C. , Marcel , S. ( 2012 ). Mobile biometrics (mobio): Joint face and voice verification for a mobile platform . IEEE Pervasive Computing , 99 . Google Scholar Sabhanayagam , T. , Venkatesan , V. P. , & Senthamaraikannan , K. ( 2018 ). A comprehensive survey on various biometric systems . International Journal of Applied Engineering Research , 13 ( 5 ), 2276 - 2297 . Google Scholar Ortiz , N. , Hernández , R. D. , Jimenez , R. , Mauledeoux , M. , & Avilés , O. ( 2018 ). Survey of biometric pattern recognition via machine learning techniques . Contemporary Engineering Sciences , 11 ( 34 ), 1677 - 1694 . 10.12988/ces.2018.84166 Google Scholar Hassanat , A. B. , Btoush , E. , Abbadi , M. A. , Al-Mahadeen , B. M. , Al-Awadi , M. , Mseidein , K. I. , … & Al-alem , F. A. ( 2017 , April). Victory sign biometrie for terrorists identification: Preliminary results . In 2017 8th International Conference on Information and Communication Systems (ICICS) (pp. 182 - 187 ). IEEE . 10.1109/IACS.2017.7921968 Google Scholar Jain , A. K. , & Li , S. Z. ( 2011 ). Handbook of Face Recognition (Vol. 1 ). New York : Springer . Google Scholar Srivastava , H. ( 2013 ). Personal identification using iris recognition system, a review . International Journal of Engineering Research and Applications (IJERA) , 3 ( 3 ), 449 - 453 . Google Scholar Safie , S. I. , Soraghan , J. J. , & Petropoulakis , L. ( 2011 ). Electrocardiogram (ECG) biometric authentication using pulse active ratio (PAR) . IEEE Transactions on Information Forensics and Security , 6 ( 4 ), 1315 - 1322 . 10.1109/TIFS.2011.2162408 Web of Science®Google Scholar Sahli , H. , Mouelhi , A. , Ben Slama , A. , Sayadi , M. , & Rachdi , R. ( 2019 ). Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images . Journal of Medical Engineering & Technology , 43 ( 5 ), 279 - 286 . 10.1080/03091902.2019.1653389 PubMedGoogle Scholar Sidek , K. A. , Mai , V. , & Khalil , I. ( 2014 ). Data mining in mobile ECG based biometric identification . Journal of Network and Computer Applications , 44 , 83 - 91 . 10.1016/j.jnca.2014.04.008 Google Scholar Benouis , M. , Mostefai , L. , Costen , N. , & Regouid , M. ( 2021 ). ECG based biometric identification using one-dimensional local difference pattern . Biomedical Signal Processing and Control , 64 , 102226 . 10.1016/j.bspc.2020.102226 Google Scholar Uwaechia , A. N. , & Ramli , D. A. ( 2021 ). A comprehensive survey on ECG signals as new biometric modality for human authentication: Recent advances and future challenges . IEEE Access . 10.1109/ACCESS.2021.3095248 Google Scholar Madduluri , S. , Kumar , T. K. , ( 2023 ). Feature selection models using 2D convolution neural network for ECG based biometric detection - A brief survey , in: 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) , Coimbatore, India , pp. 1650 - 1654 . 10.1109/ICESC57686.2023.10193501 Google Scholar Zhou , M. , Tang , Y. , Tian , Z. , & Geng , X. ( 2017 ). Semi-supervised learning for indoor hybrid fingerprint database calibration with low effort . IEEE Access , 5 , 4388 - 4400 . 10.1109/ACCESS.2017.2678603 Google Scholar Supervised and Unsupervised Data Engineering for Multimedia Data ReferencesRelatedInformation

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