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Face presentation attack detection using guided scale texture

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Abstract

Aiming to counter presentation attack (also known as spoofing attack) in face recognition system, a face presentation attack detection (also known as spoofing detection or liveness detection) scheme based on guided scale texture is proposed. In order to minimize the influence of the redundant noise contamination, guided scale space is proposed to reduce the redundancy of the original facial texture and to extract more powerful facial edges. Based on the guided scale space, two guided scale texture descriptors are proposed to extract liveness detection features, and they are guided scale based local binary pattern (GS-LBP) and local guided binary pattern (LGBP). GS-LBP takes advantage of the edge-preserving property of the guided scale space, and joint quantization is used in LGBP to encode the neighboring relationships of the original face and the guided scale face without using additional features. With the guided scale texture features, presentation attack detection is accomplished by the use of a linear support vector machine classifier. Experiments are done with public MSU MFSD, CASIA FASD, Replay-Attack and Replay-Mobile databases, and the results indicate its effectiveness. The proposed method can effectively be applied for countering photo attack and video attack in face recognition systems.

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Acknowledgements

This work was supported in part by project supported by National Natural Science Foundation of China (Grant No. 61572182, 61370225), project supported by Hunan Provincial Natural Science Foundation of China (Grant No. 15JJ2007), and supported by the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161).

The authors would like to thank Idiap research institute, Institute of Automation, Chinese Academy of Sciences (CASIA) and Michigan State University for providing the benchmark databases, and also thank Le-Bing Zhang with Hunan University for his kind proofreading of this manuscript.

The authors would like to thank the anonymous reviewers for their kind suggestions and comments.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Fei Peng & Le Qin

  2. College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410014, China

    Min Long

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  1. Fei Peng
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  2. Le Qin
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Correspondence to Fei Peng.

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Peng, F., Qin, L. & Long, M. Face presentation attack detection using guided scale texture. Multimed Tools Appl 77, 8883–8909 (2018). https://doi.org/10.1007/s11042-017-4780-0

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