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Spoof detection on face and palmprint biometrics

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Abstract

Spoofing attacks made by non-real images are a major concern to biometric systems. This paper presents a novel solution for distinguishing between live and forged identities using the fusion of texture-based methods and image quality assessment measures. In our approach, we used LBP and HOG texture descriptors to extract texture information of an image. Additionally, feature space of seven full-reference complementary image quality measures is considered including peak signal-to-noise ratio, structural similarity, mean-squared error, normalized cross-correlation, maximum difference, normalized absolute error and average difference. We built a palmprint spoof database made by printed palmprint photograph of PolyU palmprint database using camera. Experimental results on three public-domain face spoof databases (Idiap Print-Attack, Replay-Attack and MSU MFSD) and palmprint spoof database show that the proposed solution is effective in face and palmprint spoof detection.

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Acknowledgements

The authors would like to thank Dr. Ajay Kumar of IIT Delhi for sharing PolyU palmprint database. We also give our sincere appreciation to Dr. Sebastien Marcel and Dr. Andre Anjos from Idiap Research Institute for having provided us with Print-Attack and Replay-Attack databases. Furthermore, we would like to express our best regards to Dr. Di Wen from the Michigan State University Pattern Recognition and Image Processing (PRIP) Laboratory for offering us MSU face database. Last but not least, we would like to thank the anonymous reviewers and the editor for providing constructive comments and suggestions that have contributed to the improvement in the quality and presentation of this paper.

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  1. Computer Engineering Department, Eastern Mediterranean University, via Mersin 10, Famagusta, North Cyprus, Turkey

    Mina Farmanbar & Önsen Toygar

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  1. Mina Farmanbar
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  2. Önsen Toygar
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Correspondence to Önsen Toygar.

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Farmanbar, M., Toygar, Ö. Spoof detection on face and palmprint biometrics. SIViP 11, 1253–1260 (2017). https://doi.org/10.1007/s11760-017-1082-y

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