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Face Anti-Spoofing via Disentangled Representation Learning

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12364)

Abstract

Face anti-spoofing is crucial to security of face recognition systems. Previous approaches focus on developing discriminative models based on the features extracted from images, which may be still entangled between spoof patterns and real persons. In this paper, motivated by the disentangled representation learning, we propose a novel perspective of face anti-spoofing that disentangles the liveness features and content features from images, and the liveness features is further used for classification. We also put forward a Convolutional Neural Network (CNN) architecture with the process of disentanglement and combination of low-level and high-level supervision to improve the generalization capabilities. We evaluate our method on public benchmark datasets and extensive experimental results demonstrate the effectiveness of our method against the state-of-the-art competitors. Finally, we further visualize some results to help understand the effect and advantage of disentanglement.

Keywords

Face anti-spoofing Generative model Disentangled representation 

Supplementary material

504475_1_En_38_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (pdf 1578 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.East China Normal UniversityShanghaiChina
  2. 2.Youtu LabTencentChina

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