Face Liveness Detection Based on the Improved CNN with Context and Texture Information

GAO Chenqiang; LI Xindou; ZHOU Fengshun; MU Song

doi:10.1049/cje.2019.07.012

Volume 28 Issue 6

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GAO Chenqiang, LI Xindou, ZHOU Fengshun, et al. “Face Liveness Detection Based on the Improved CNN with Context and Texture Information”. Chinese Journal of Electronics, vol. 28 no. 6. doi: 10.1049/cje.2019.07.012

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GAO Chenqiang, LI Xindou, ZHOU Fengshun, et al. “Face Liveness Detection Based on the Improved CNN with Context and Texture Information”. Chinese Journal of Electronics, vol. 28 no. 6. doi: 10.1049/cje.2019.07.012

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Face Liveness Detection Based on the Improved CNN with Context and Texture Information

doi: 10.1049/cje.2019.07.012

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing 400065, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61571071) and Chongqing Research Program of Basic Research and Frontier Technology (No.cstc2018jcyjAX0227).

Received Date: 2018-10-08
Rev Recd Date: 2019-04-29
Publish Date: 2019-11-10

Abstract

Abstract

Face liveness detection, as a key module of real face recognition systems, is to distinguish a fake face from a real one. In this paper, we propose an improved Convolutional neural network (CNN) architecture with two bypass connections to simultaneously utilize low-level detailed information and high-level semantic information. Considering the importance of the texture information for describing face images, texture features are also adopted under the conventional recognition framework of Support vector machine (SVM). The improved CNN and the texture feature based SVM are fused. Context information which is usually neglected by existing methods is well utilized in this paper. Two widely used datasets are used to test the proposed method. Extensive experiments show that our method outperforms the state-of-the-art methods.
- Face liveness detection,
- Deep learning,
- Context information,
- Texture information

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References(26)

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