A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations, and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing Mean-Closure Weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from different matchers for each modality are combined using MCW score fusion method. The MCW technique achieves the optimal weight for each matcher involved in the score computation. Further, DS theory is applied to the induced scores to output the final decision. The rigorous experimental evaluations on three virtual databases indicate that the proposed hybrid fusion framework outperforms over the component level or individual fusion methods (score level and decision level fusion). As a result, we achieve (48%, 66%), (72%, 86%) and (49%, 38%) of performance improvement over unimodal cancelable iris and unimodal cancelable fingerprint verification systems for Virtual_A, Virtual_B, and Virtual_C databases, respectively. Also, the proposed method is robust enough to the variability of scores and outliers satisfying the requirement of secure authentication.

[1]  Jiankun Hu,et al.  A fingerprint and finger-vein based cancelable multi-biometric system , 2018, Pattern Recognit..

[2]  Marina L. Gavrilova,et al.  Multimodal Cancelable Biometrics , 2012, 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing.

[3]  Anil K. Jain,et al.  FVC2000: Fingerprint Verification Competition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Stathes Hadjiefthymiades,et al.  Distributed Localized Contextual Event Reasoning Under Uncertainty , 2017, IEEE Internet of Things Journal.

[5]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Ajay Kumar,et al.  Adaptive management of multimodal biometrics fusion using ant colony optimization , 2016, Inf. Fusion.

[7]  Aditya Prasad,et al.  A privacy-preserving cancelable iris template generation scheme using decimal encoding and look-up table mapping , 2017, Comput. Secur..

[8]  Anil K. Jain,et al.  Encyclopedia of Biometrics , 2015, Springer US.

[9]  M. Sujatha,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2015 .

[10]  Madasu Hanmandlu,et al.  Hybrid fusion of score level and adaptive fuzzy decision level fusions for the finger-knuckle-print based authentication , 2015, Appl. Soft Comput..

[11]  Raymond N. J. Veldhuis,et al.  Hybrid fusion for biometrics: Combining score-level and decision-level fusion , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[12]  Sridha Sridharan,et al.  Score-Level Multibiometric Fusion Based on Dempster–Shafer Theory Incorporating Uncertainty Factors , 2015, IEEE Transactions on Human-Machine Systems.

[13]  Christoph Busch,et al.  Towards cancelable multi-biometrics based on bloom filters: a case study on feature level fusion of face and iris , 2015, 3rd International Workshop on Biometrics and Forensics (IWBF 2015).

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

[15]  Andrew Beng Jin Teoh,et al.  Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion , 2014, Inf. Fusion.

[16]  Julien Bringer,et al.  A Framework for Analyzing Template Security and Privacy in Biometric Authentication Systems , 2012, IEEE Transactions on Information Forensics and Security.

[17]  Somnath Dey,et al.  Securing fingerprint template using noninvertible ridge feature transformation , 2018, J. Electronic Imaging.

[18]  Samy Bengio,et al.  A statistical significance test for person authentication , 2004, Odyssey.

[19]  Patrizio Campisi,et al.  Hill-Climbing Attacks on Multibiometrics Recognition Systems , 2015, IEEE Transactions on Information Forensics and Security.

[20]  Stathes Hadjiefthymiades,et al.  Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[21]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[22]  Pingzhi Fan,et al.  Performance evaluation of score level fusion in multimodal biometric systems , 2010, Pattern Recognit..

[23]  Sébastien Marcel,et al.  Anti-spoofing: Evaluation Methodologies , 2014 .

[24]  M.N.S. Swamy,et al.  Normalization and Weighting Techniques Based on Genuine-Impostor Score Fusion in Multi-Biometric Systems , 2018, IEEE Transactions on Information Forensics and Security.

[25]  Gregory M. Provan,et al.  A logic-based analysis of Dempster-Shafer theory , 1990, Int. J. Approx. Reason..

[26]  Anne M. P. Canuto,et al.  Investigating fusion approaches in multi-biometric cancellable recognition , 2013, Expert Syst. Appl..

[27]  P. Smets Decision Making in a Context where Uncertainty is Represented by Belief Functions , 2002 .

[28]  Sanjay Kumar Singh,et al.  Construction of a Bayesian decision theory-based secure multimodal fusion framework for soft biometric traits , 2018, IET Biom..

[29]  Anil K. Jain,et al.  Biometric Template Protection: Bridging the performance gap between theory and practice , 2015, IEEE Signal Processing Magazine.

[30]  Fella Hachouf,et al.  Score-Level Fusion of Face and Voice Using Particle Swarm Optimization and Belief Functions , 2015, IEEE Transactions on Human-Machine Systems.

[31]  Nasir D. Memon,et al.  Secure Biometric Templates from Fingerprint-Face Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Andrew Beng Jin Teoh,et al.  Enhanced multi-line code for minutiae-based fingerprint template protection , 2013, Pattern Recognit. Lett..

[33]  Somnath Dey,et al.  Multimodal biometrics: state of the art in fusion techniques , 2009, Int. J. Biom..

[34]  Arun Ross,et al.  On Mixing Fingerprints , 2013, IEEE Transactions on Information Forensics and Security.

[35]  Anil K. Jain,et al.  Decision-Level Fusion in Fingerprint Verification , 2001, Multiple Classifier Systems.

[36]  Chen Qixiang,et al.  An Improved Algorithm for Dempster-Shafer Theory of Evidence , 2009, 2009 International Conference on Electronic Commerce and Business Intelligence.

[37]  Stelvio Cimato,et al.  Privacy-Aware Biometrics: Design and Implementation of a Multimodal Verification System , 2008, 2008 Annual Computer Security Applications Conference (ACSAC).

[38]  C. Busch,et al.  Multi-algorithm fusion with template protection , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[39]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[40]  Somnath Dey,et al.  A non-invertible cancelable fingerprint template generation based on ridge feature transformation , 2018, ArXiv.

[41]  Alisher Kholmatov,et al.  Multi-biometric templates using fingerprint and voice , 2008, SPIE Defense + Commercial Sensing.

[42]  Masanori Mizoguchi,et al.  Vocabulary harmonisation for biometrics: the development of ISO/IEC 2382 Part 37 , 2014, IET Biom..

[43]  Stathes Hadjiefthymiades,et al.  Contextual Reasoning under Uncertainty in Sensor Data Stream Monitoring , 2015, Int. J. Monit. Surveillance Technol. Res..

[44]  Somnath Dey,et al.  Score-level fusion for cancelable multi-biometric verification , 2019, Pattern Recognit. Lett..