Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 443-447.doi: 10.11896/jsjkx.200900207

• Information Security • Previous Articles     Next Articles

Face Anti-spoofing Algorithm for Noisy Environment

ZHUO Ya-qian, OU Bo   

  1. College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZHUO Ya-qian,born in 1997,postgra-duate.Her main research interests include information security and image processing.
    OU Bo,born in 1985,Ph.D,associate professor,Ph.D supervisor.His main research interests include information security and digital watermarking.
  • Supported by:
    National Natural Science Foundation of China(61502160) and Hunan Provincial Natural Science Foundation of China(2018JJ3078).

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Abstract: In the era of intelligent,face recognition algorithm is one of the key technologies for smart identity authentication,and widely used in the fields of access control,mobile phone unlocking,and financial payment.Face anti-spoofing recognition is used to identify the real face and resist the fake face attacks.Among the existing methods,local binary pattern(LBP) can provide a good anti-spoofing performance in practice,but its recognition performance in noisy scenes can be improved.For this reason,we proposed a pairwise local binary pattern (PLBP) based on adjacent pixel pairs,which can improve the performance in noisy environments by exploiting the correlations between pixel pairs.Compared with LBP-based methods,the proposed algorithm compared the mean value of adjacent pixel pairs with neighboring pixels to generate a binary pattern,so that the spatial correlations between pixel pairs can be used to obtain more facial features.Experiment results show that the performance of the proposed method is better than the current mainstream LBP-based methods,the accuracy rate is nearly 95.05% under noise-free conditions.Our method also can reduce performance loss under Gaussian noise environment and provide a stronger robustness.

Key words: Face anti-spoofing, Pairwise local binary pattern, Support vector machine

CLC Number: 

  • TP399
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