Anti-spoofing method for fingerprint recognition using patch based deep learning machine

Abstract Today’s with increasing identity theft, biometric systems based on fingerprints have a growing importance in protection and access restrictions. Malicious users violate them by presenting fabricated attempts. For example, artificial fingerprints constructed by gelatin, Play-Doh and Silicone molds may be misused for access and identity fraud by forgers to clone fingerprints. This process is called spoofing. To detect such forgeries, some existing methods using handcrafted descriptors have been implemented for assuring user presence. Most of them give low accuracy rates in recognition. The proposed method used Discriminative Restricted Boltzmann Machines to recognize fingerprints accurately against fabricated materials used for spoofing.

[1]  Anil K. Jain,et al.  RaspiReader: Open Source Fingerprint Reader , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Yang Gao,et al.  Intermediate Spoofing Strategies and Countermeasures , 2013 .

[3]  Gian Luca Marcialis,et al.  Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015 , 2016, Image Vis. Comput..

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

[5]  Chris Roberts,et al.  Biometric attack vectors and defences , 2007, Comput. Secur..

[6]  Yu Xie,et al.  Fake Fingerprint Detection Based on Wavelet Analysis and Local Binary Pattern , 2014, CCBR.

[7]  Stephanie Schuckers,et al.  Fingerprint Liveness Detection Using Local Ridge Frequencies and Multiresolution Texture Analysis Techniques , 2006, 2006 International Conference on Image Processing.

[8]  Sébastien Marcel,et al.  LBP - TOP Based Countermeasure against Face Spoofing Attacks , 2012, ACCV Workshops.

[9]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[10]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[11]  Roberto de Alencar Lotufo,et al.  Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns , 2014, 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings.

[12]  Suneeta Agarwal,et al.  Fingerprint Liveness Detection Using Curvelet Energy and Co-Occurrence Signatures , 2008, 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation.

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

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

[15]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[16]  Gian Luca Marcialis,et al.  Multimodal Anti-spoofing in Biometric Recognition Systems , 2014, Handbook of Biometric Anti-Spoofing.

[17]  Dario Maio,et al.  Fake Fingerprint Detection by Odor Analysis , 2006, ICB.

[18]  Heung-Kyu Lee,et al.  Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks , 2017, ICISA.

[19]  Weihong Deng,et al.  Learning temporal features using LSTM-CNN architecture for face anti-spoofing , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[20]  Xin Liu,et al.  Spoof Fingerprint Detection based on Co-occurrence Matrix , 2015 .

[21]  Christophe Champod,et al.  Fingerprints and Other Ridge Skin Impressions, Second Edition , 2016 .

[22]  David Menotti,et al.  Deep Representations for Iris, Face, and Fingerprint Spoofing Detection , 2014, IEEE Transactions on Information Forensics and Security.

[23]  Allen Y. Yang,et al.  Fingerprint liveness detection based on histograms of invariant gradients , 2014, IEEE International Joint Conference on Biometrics.

[24]  Christoph Busch,et al.  Presentation attack detection methods for fingerprint recognition systems: a survey , 2014, IET Biom..

[25]  Ines Goicoechea-Telleria,et al.  Presentation Attack Detection Evaluation on Mobile Devices: Simplest Approach for Capturing and Lifting a Latent Fingerprint , 2018, 2018 International Carnahan Conference on Security Technology (ICCST).

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

[27]  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).

[28]  Arun Ross,et al.  Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials , 2015, IEEE Transactions on Information Forensics and Security.

[29]  Anil K. Jain,et al.  Fingerprint spoof detection using minutiae-based local patches , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[30]  Wei-Yun Yau,et al.  Person recognition by fusing palmprint and palm vein images based on "Laplacianpalm" representation , 2008, Pattern Recognit..

[31]  David G. Lowe,et al.  Scalable Nearest Neighbor Algorithms for High Dimensional Data , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Geoffrey E. Hinton,et al.  A New Learning Algorithm for Mean Field Boltzmann Machines , 2002, ICANN.

[33]  Arun Ross,et al.  Automatic adaptation of fingerprint liveness detector to new spoof materials , 2014, IEEE International Joint Conference on Biometrics.

[34]  Gian Luca Marcialis,et al.  LivDet 2017 Fingerprint Liveness Detection Competition 2017 , 2018, 2018 International Conference on Biometrics (ICB).

[35]  Gian Luca Marcialis,et al.  Fingerprint liveness detection by local phase quantization , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[36]  Gian Luca Marcialis,et al.  Review of Fingerprint Presentation Attack Detection Competitions , 2019, Handbook of Biometric Anti-Spoofing, 2nd Ed..

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

[38]  Gian Luca Marcialis,et al.  Evaluation of multimodal biometric score fusion rules under spoof attacks , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

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

[40]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[41]  Jaihie Kim,et al.  An incremental learning method for spoof fingerprint detection , 2019, Expert Syst. Appl..

[42]  Eui Chul Lee,et al.  Statistical anti-spoofing method for fingerprint recognition , 2018, Soft Comput..

[43]  Vrizlynn L. L. Thing,et al.  Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis , 2016, IEEE Transactions on Information Forensics and Security.

[44]  Kevin W. Bowyer,et al.  Face recognition technology: security versus privacy , 2004, IEEE Technology and Society Magazine.

[45]  Anil K. Jain,et al.  Fingerprint Spoof Buster: Use of Minutiae-Centered Patches , 2018, IEEE Transactions on Information Forensics and Security.

[46]  Julian Fiérrez,et al.  Author's Personal Copy Future Generation Computer Systems a High Performance Fingerprint Liveness Detection Method Based on Quality Related Features , 2022 .