Effectiveness of symmetric rejection for a secure and user convenient multistage biometric system

A multistage biometric verification system uses multiple biometrics and/or multiple biometric verifiers to generate a verification decision. The core of a multistage biometric verification system is reject option which allows a stage not to give a genuine/impostor decision when it is not confident enough. This paper studies the effectiveness of symmetric rejection for multistage biometric verification systems. The symmetric rejection method determines the reject region by symmetrically rejecting equal proportion of genuine and impostor scores. The applicability of a multistage biometric verification system depends on how secure and user convenient it is, which is measured by the performance–cost trade-off. This paper analyzes the performance–cost trade-off of symmetric rejection method by conducting extensive experiments. Experiments are performed on two biometric databases: (1) publicly available NIST database and (2) a keystroke database. In addition, the symmetric rejection method is empirically compared with two existing rejection methods: (1) sequential probability ratio test-based method, which uses score-fusion and (2) Marcialis et al.’s method, which does not use score fusion. Results demonstrate strong effect of symmetric rejection method on creating a secure and user convenient multistage biometric verification system.

[1]  Abdelhani Boukrouche,et al.  Multimodal biometric recognition using human ear and palmprint , 2017, IET Biom..

[2]  Lavinia Mihaela Dinca,et al.  The Fall of One, the Rise of Many A Survey on Multi-Biometric Fusion Methods , 2017 .

[3]  Somnath Dey,et al.  A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification , 2018, Applied Intelligence.

[4]  Valentina Emilia Balas,et al.  8-valent fuzzy logic for iris recognition and biometry , 2011, 2011 5th International Symposium on Computational Intelligence and Intelligent Informatics (ISCIII).

[5]  Sung Wook Baik,et al.  CNN-based anti-spoofing two-tier multi-factor authentication system , 2019, Pattern Recognit. Lett..

[6]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Loris Nanni,et al.  Overview of the combination of biometric matchers , 2017, Inf. Fusion.

[8]  Md. Saiful Islam,et al.  Improved sequential fusion of heart-signal and fingerprint for anti-spoofing , 2018, 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA).

[9]  Tieniu Tan,et al.  Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices , 2018, IEEE Transactions on Information Forensics and Security.

[10]  Carla M. Santos-Pereira,et al.  On optimal reject rules and ROC curves , 2005, Pattern Recognit. Lett..

[11]  Andrew R. Barron,et al.  Mixture Density Estimation , 1999, NIPS.

[12]  Bernadette Dorizzi,et al.  Tuning cost and performance in multi-biometric systems: A novel and consistent view of fusion strategies based on the Sequential Probability Ratio Test (SPRT) , 2010, Pattern Recognit. Lett..

[13]  Khandaker Abir Rahman,et al.  An Empirical Study on Verifier Order Selection in Serial Fusion Based Multi-biometric Verification System , 2017, IEA/AIE.

[14]  Robert P. W. Duin,et al.  The interaction between classification and reject performance for distance-based reject-option classifiers , 2006, Pattern Recognit. Lett..

[15]  Richa Singh,et al.  Context Switching Algorithm for Selective Multibiometric Fusion , 2009, PReMI.

[16]  Peter L. Bartlett,et al.  Classification with a Reject Option using a Hinge Loss , 2008, J. Mach. Learn. Res..

[17]  Mohamed Abdel-Mottaleb,et al.  Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition , 2016, IEEE Transactions on Information Forensics and Security.

[18]  Nasser M. Nasrabadi,et al.  Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[19]  Ramachandra Raghavendra,et al.  Designing efficient fusion schemes for multimodal biometric systems using face and palmprint , 2011, Pattern Recognit..

[20]  Stanley S. Ipson,et al.  A multi-biometric iris recognition system based on a deep learning approach , 2017, Pattern Analysis and Applications.

[21]  Kenta Takahashi,et al.  Optimal sequential fusion for multibiometric cryptosystems , 2016, Inf. Fusion.

[22]  Mohamed Hammad,et al.  Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network , 2019, Comput. Secur..

[23]  C. K. Chow,et al.  On optimum recognition error and reject tradeoff , 1970, IEEE Trans. Inf. Theory.

[24]  Md Hossain,et al.  On enhancing serial fusion based multi-biometric verification system , 2018, Applied Intelligence.

[25]  Fabio Roli,et al.  Personal identity verification by serial fusion of fingerprint and face matchers , 2009, Pattern Recognit..

[26]  Kenta Takahashi,et al.  A secure and user-friendly multimodal biometric system , 2004, SPIE Defense + Commercial Sensing.

[27]  Francesco Tortorella.  An Optimal Reject Rule for Binary Classifiers , 2000, SSPR/SPR.

[28]  Kanta Matsuura,et al.  Towards optimal countermeasures against wolves and lambs in biometrics , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[29]  Richa Singh,et al.  Fusion of Handcrafted and Deep Learning Features for Large-Scale Multiple Iris Presentation Attack Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[30]  Qing Zhang,et al.  A Novel Serial Multimodal Biometrics Framework Based on Semisupervised Learning Techniques , 2014, IEEE Transactions on Information Forensics and Security.

[31]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[32]  Sherif N. Abbas,et al.  A new multi-level approach to EEG based human authentication using eye blinking , 2016, Pattern Recognit. Lett..

[33]  Albert Ali Salah,et al.  Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms , 2009, IEEE Transactions on Information Forensics and Security.

[34]  Katarzyna Harezlak,et al.  Fusion of eye movement and mouse dynamics for reliable behavioral biometrics , 2018, Pattern Analysis and Applications.

[35]  Vir V. Phoha,et al.  New impostor score based rejection methods for continuous keystroke verification with weak templates , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[36]  Matthew C. Valenti,et al.  Multibiometric secure system based on deep learning , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[37]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[38]  Claudia Picardi,et al.  Keystroke analysis of free text , 2005, TSEC.

[39]  Mohamed Elhoseny,et al.  Cascade Multimodal Biometric System Using Fingerprint and Iris Patterns , 2017, AISI.

[40]  Sukhendu Das,et al.  Score-level fusion using generalized extreme value distribution and DSmT, for multi-biometric systems , 2018, IET Biom..

[41]  Himanshu S. Bhatt,et al.  QFuse: Online learning framework for adaptive biometric system , 2015, Pattern Recognit..

[42]  Farbod Razzazi,et al.  A New Framework for Match on Card and Match on Host Quality Based Multimodal Biometric Authentication , 2018, Journal of Signal Processing Systems.

[43]  Gian Luca Marcialis,et al.  Evaluation of serial and parallel multibiometric systems under spoofing attacks , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[44]  Nicolaie Popescu-Bodorin,et al.  Iris recognition with 4 or 5 fuzzy sets , 2015, IFSA-EUSFLAT.

[45]  Mohamed Abdel-Mottaleb,et al.  Multimodal Biometrics Recognition from Facial Video via Deep Learning , 2017 .

[46]  Arun Ross,et al.  A framework for quality-based biometric classifier selection , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[47]  Gian Luca Marcialis,et al.  Serial fusion of multi-modal biometric systems , 2010, 2010 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications.

[48]  Julian Fiérrez,et al.  Multiple classifiers in biometrics. part 1: Fundamentals and review , 2018, Inf. Fusion.

[49]  Kiran B. Raja,et al.  Multi-modal authentication system for smartphones using face, iris and periocular , 2015, 2015 International Conference on Biometrics (ICB).

[50]  Arun Ross,et al.  On the Dynamic Selection of Biometric Fusion Algorithms , 2010, IEEE Transactions on Information Forensics and Security.

[51]  S. R. Mahadeva Prasanna,et al.  Development of Multi-Level Speech based Person Authentication System , 2017, J. Signal Process. Syst..

[52]  Yosuke Kaga,et al.  On restricting modalities in likelihood-ratio based biometric score fusion , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[53]  Steven Furnell,et al.  Continuous user authentication using multi-modal biometrics , 2015, Comput. Secur..

[54]  Rama Chellappa,et al.  HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[55]  Alberto Botana López,et al.  Deep Learning in Biometrics: A Survey , 2019, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal.

[56]  Anil K. Jain,et al.  Likelihood Ratio-Based Biometric Score Fusion , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Julian Fiérrez,et al.  Multiple classifiers in biometrics. Part 2: Trends and challenges , 2018, Inf. Fusion.

[58]  Ahmed Bouridane,et al.  Cascaded multimodal biometric recognition framework , 2014, IET Biom..

[59]  D. Panchenko,et al.  Risk bounds for mixture density estimation , 2005 .

[60]  Vir V. Phoha,et al.  On Controlling Genuine Reject Rate in Multi-stage Biometric Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[61]  Fabio Roli,et al.  Reject option with multiple thresholds , 2000, Pattern Recognit..