A novel face anti-spoofing method using multiple color space models

Tingting Li; Zhichao Lian

doi:10.1117/12.2589447

27 January 2021 A novel face anti-spoofing method using multiple color space models

Tingting Li, Zhichao Lian

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172021 (2021) https://doi.org/10.1117/12.2589447
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

The importance of face recognition algorithms in biometric authentication systems has become increasingly prominent. In order to ensure the security of face authentication, it is crucial to detect spoof attacks before performing face recognition. In this paper, we propose a 9-layer convolutional neural network (CNN) architecture using end-to-end learning for face anti-spoofing applications in small-scale datasets, which can directly judge the corresponding output class of the raw input face image. In addition, we believe that real faces and fake faces are well distinguishable in color spaces other than the RGB space. Therefore, we propose a novel face anti-spoofing method using multiple color space models to provide complementary features. Extensive experiments on mixed dataset for CASIA-FASD database and Replay-Attack database showed excellent face spoofing detection result comparing with other similar approaches.

Citation Download Citation

Tingting Li and Zhichao Lian "A novel face anti-spoofing method using multiple color space models", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172021 (27 January 2021); https://doi.org/10.1117/12.2589447

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KEYWORDS

Facial recognition systems

RGB color model

Databases

Video

Convolution

Network architectures

Image processing

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