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Fusing Multiple Deep Features for Face Anti-spoofing

  • Yan Tang
  • Xing Wang
  • Xi Jia
  • Linlin Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

With the growing deployment of face recognition system in recent years, face anti-spoofing has become increasingly important, due to the increasing number of spoofing attacks via printed photos or replayed videos. Motivated by the powerful representation ability of deep learning, in this paper we propose to use CNNs (Convolutional Neural Networks) to learn multiple deep features from different cues of the face images for anti-spoofing. We integrate temporal features, color based features and patch based local features for spoof detection. We evaluate our approach extensively on publicly available databases like CASIA FASD, REPLAY-MOBILE and OULU-NPU. The experimental results show that our approach can achieve much better performance than state-of-the-art methods. Specifically, 2.22% of EER (Equal Error Rate) on the CASIA FASD, 3.2% of ACER (Average Classification Error Rate) on the OULU-NPU (protocol 1) and 0.00% of ACER on the REPLAY-MOBILE database are achieved.

Keywords

Deep convolutional neural networks Face anti-spoofing Multiple features 

Notes

Acknowledgments

The work is supported by Natural Science Foundation of China under grands No. 61672357 and U1713214.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computer Vision Institute, School of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

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