Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks

DOI

10.2991/ijcis.d.210419.003How to use a DOI?

Keywords

Face spoof attack detection; Multiple-feature learning; Capsule neural networks; Hypergraph regularization

Abstract

Face authentication has been widely used in personal identification. However, face authentication systems can be attacked by fake images. Existing methods try to detect such attacks with different features. Among them, using color images become popular since it is flexible and generic. In this paper, a novel feature representation for face spoof attack detection, namely hypergraph capsule convolutional neural networks (HGC-CNNs), is proposed, which takes advantage of multiple features. To achieve it, capsule neural networks are used to integrate different types of features. In addition, hypergraph regularization is applied to learn the correlations among samples. In this way, the descriptive power is improved. The proposed feature representation is compared with existing features for face spoof attack detection and experimental results on two commonly used datasets emphasize the effectiveness of HGC-CNN.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yuxin Liang
AU  - Chaoqun Hong
AU  - Weiwei Zhuang
PY  - 2021
DA  - 2021/04/26
TI  - Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 1396
EP  - 1402
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210419.003
DO  - 10.2991/ijcis.d.210419.003
ID  - Liang2021
ER  -