A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Despite emerging as a prominent choice to serve the security concerns of person authentication applications, unimodal biometric systems are vulnerable to spoof attacks. Multimodal biometric systems can effectively minimize spoof attacks while improving the overall performance. In this paper, we present a multimodal system based on two modalities derived from multi instance fingerprint acquisition viz. fingerprint and the associated time dynamics. Extensive user verification and spoof resistance experiments conducted on virtual multimodal databases, created by combining ATVS and LivDet-13 fingerprint databases each with fingerprint dynamics database. Fusion is performed at match score level using sum and weighted sum rules. The empirical results demonstrate spoof resistance of the proposed multimodal system with significant performance improvement over unimodal and multi-instance fingerprint recognition systems. The performance of the proposed system is evaluated on well-known metrics like Detection Error Trade-off (DET) curves, equal error rate (EER), and Area Under the Curve (AUC). Display Omitted Multimodal system based on physiological and behavioral characteristics is proposed.Experiments are conducted in scenarios: unimodal, multi-instance and multimodal.The system performance evaluation is performed in the presence of spoof samples.Average relative improvement in EER over unimodal is 90.64%.Average relative improvement in EER over multi-instance system is 82.57%.

[1]  Jugurta R. Montalvão Filho,et al.  Contributions to empirical analysis of keystroke dynamics in passwords , 2015, Pattern Recognit. Lett..

[2]  Javier Garrido Salas,et al.  BiosecurID: a multimodal biometric database , 2009, Pattern Analysis and Applications.

[3]  Sébastien Marcel,et al.  Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition , 2014, IEEE Transactions on Image Processing.

[4]  Kenneth Ko,et al.  User's Guide to NIST Biometric Image Software (NBIS) , 2007 .

[5]  Sethuraman Panchanathan,et al.  Generalized batch mode active learning for face-based biometric recognition , 2013, Pattern Recognit..

[6]  Carlo Sansone,et al.  Liveness-based fusion approaches in multibiometrics , 2012, 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings.

[7]  Qijun Zhao,et al.  A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy , 2015, CCBR.

[8]  Sébastien Marcel,et al.  Handbook of Biometric Anti-Spoofing , 2019, Advances in Computer Vision and Pattern Recognition.

[9]  Gian Luca Marcialis,et al.  LivDet 2013 Fingerprint Liveness Detection Competition 2013 , 2013, 2013 International Conference on Biometrics (ICB).

[10]  Chin-Chuan Han,et al.  Personal authentication using palm-print features , 2003, Pattern Recognit..

[11]  Alvin F. Martin,et al.  The DET curve in assessment of detection task performance , 1997, EUROSPEECH.

[12]  Carlo Sansone,et al.  Combining perspiration- and morphology-based static features for fingerprint liveness detection , 2012, Pattern Recognit. Lett..

[13]  Petru Radu,et al.  Robust multimodal face and fingerprint fusion in the presence of spoofing attacks , 2016, Pattern Recognit..

[14]  Woo Chaw Seng,et al.  A review of biometric technology along with trends and prospects , 2014, Pattern Recognit..

[15]  Julian Fiérrez,et al.  A Comparative Study of Fingerprint Image-Quality Estimation Methods , 2007, IEEE Transactions on Information Forensics and Security.

[16]  Julian Fiérrez,et al.  Evaluation of direct attacks to fingerprint verification systems , 2011, Telecommun. Syst..

[17]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[18]  Arun Ross,et al.  50 years of biometric research: Accomplishments, challenges, and opportunities , 2016, Pattern Recognit. Lett..

[19]  Miguel Angel Ferrer-Ballester,et al.  A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics , 2016, Pattern Recognit. Lett..

[20]  Miguel Angel Ferrer-Ballester,et al.  On-line signature recognition through the combination of real dynamic data and synthetically generated static data , 2015, Pattern Recognit..

[21]  Chun Liu,et al.  An evaluation of fake fingerprint databases utilizing SVM classification , 2015, Pattern Recognit. Lett..

[22]  Bernadette Dorizzi,et al.  Guide to Biometric Reference Systems and Performance Evaluation , 2009 .

[23]  Javier Galbally.  Anti-spoofing: Fingerprint Databases , 2014 .

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

[25]  Arun Ross,et al.  A Survey on Anti-Spoofing Schemes for Fingerprint Recognition Systems , 2014 .

[26]  K. Harshika.  Image Quality Assessment for Fake Biometric Detection : Application to Iris , Fingerprint , and Face Recognition , 2017 .

[27]  Francisco Herrera,et al.  A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation , 2015, Inf. Sci..

[28]  Ishan Bhardwaj,et al.  A Novel Behavioural Biometric Technique for Robust User Authentication , 2017 .

[29]  Johannes Peltola,et al.  Soft biometrics - combining body weight and fat measurements with fingerprint biometrics , 2006, Pattern Recognit. Lett..

[30]  L. Biel,et al.  ECG analysis: a new approach in human identification , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[31]  Anil K. Jain,et al.  Altered Fingerprints: Analysis and Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Christophe Champod,et al.  Vulnerabilities of fingerprint reader to fake fingerprints attacks. , 2011, Forensic science international.

[33]  Salman Yussof,et al.  Integration of multiple soft biometrics for human identification , 2015, Pattern Recognit. Lett..

[34]  Anil K. Jain,et al.  Can soft biometric traits assist user recognition? , 2004, SPIE Defense + Commercial Sensing.

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

[36]  Andrew Beng Jin Teoh,et al.  Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template , 2016, Pattern Recognit..

[37]  Arun Ross,et al.  Multibiometric systems , 2004, CACM.

[38]  Einar Snekkenes,et al.  Spoof Attacks on Gait Authentication System , 2007, IEEE Transactions on Information Forensics and Security.

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

[40]  Alessandra Lumini,et al.  An evaluation of direct attacks using fake fingers generated from ISO templates , 2010, Pattern Recognit. Lett..

[41]  Anil K. Jain,et al.  Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Abdenour Hadid,et al.  Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned , 2015, IEEE Signal Processing Magazine.

[43]  Venu Govindaraju,et al.  Behavioural biometrics: a survey and classification , 2008, Int. J. Biom..