A comparative study of handcrafted local texture descriptors for fingerprint liveness detection under real world scenarios

Abstract

Authentication using fingerprints is widely deployed in various applications to ensure a secure and efficient method for access control. However, fingerprint recognition systems can be deceived by spoofing attacks. Therefore, it is necessary to ensure the security of fingerprint-based recognition system using liveness detection. The work presented in this paper evaluates the potential of various handcrafted texture features under cross-dataset, cross-sensor, cross-material, unknown-material, and combined datasets experimental scenarios. We have considered Binarized Statistical Image Features (BSIF), Local Phase Quantization (LPQ), Weber Local Descriptor (WLD), Local Contrast Phase Descriptor (LCPD), and Rotation Invariant Co-occurrence among adjacent Local Binary Pattern (RicLBP) for liveness detection of fingerprint images. The performance of these descriptors against novel spoof materials, different sensors, and different acquisition environments reflect their robustness under real world attack scenarios. The experimental evaluations are performed on LivDet 2011, 2013, and 2015 databases using Support Vector Machine (SVM) classifier. The experimental evaluation shows that LCPD and WLD are the most effective descriptors for liveness detection under diverse testing conditions. The comparative performance evaluation of these handcrafted texture features with learning based features also indicate their effectiveness in real world attack scenarios. Experimental evaluation using the combination of best performing LCPD and WLD features further improve the performance of fingerprint liveness detection. The experimental outcome of the current research clearly indicates the superiority of handcrafted local texture descriptor in the real world presentation attack scenarios. Also, it is advantageous to use local texture descriptor as they provide a simple and faster approach for fingerprint liveness detection in real world applications of fingerprint recognition systems. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.