SA-UNet for face anti-spoofing with depth estimation

Junting Chen; Sijie Niu; Xizhan Gao; Shenyuan Li; Jiwen Dong

doi:10.1117/12.2623137

16 February 2022 SA-UNet for face anti-spoofing with depth estimation

Junting Chen, Sijie Niu, Xizhan Gao, Shenyuan Li, Jiwen Dong

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831R (2022) https://doi.org/10.1117/12.2623137
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

With the large number of face recognition devices deployed in real application scenarios, face anti-spoofing has become a hot topic nowadays. Previous methods are mostly based on handcrafted features, while recent methods are mostly based on neural networks. However, both the traditional hand-crafted based method and the deep learning methods are still faced with the problem of insufficient generalization ability. In traditional deep learning methods for classification tasks, the label of samples is often a code of the category name. Recent studies have also shown that besides color and distortion, the depth information of face is also considered as an important clue to distinguish real and fake face. In order to combine prior knowledge of face depth information with deep learning method, it is a way worth exploring to expand the label information by using estimated depth image labels instead of coding labels. In this paper, we proposed an auxiliary supervised method to extend label information by using estimated depth information of face. A SA-UNet model which combined spatial attention modules with classic UNet is proposed to generate the depth estimation image for face anti-spoofing. Moreover, contrast depth loss is introduced to focus on the neighborhood information of the pixels, and a scoring method based on the proportion of non-background area is proposed to do the classification. In order to measure the generalization ability of our method, we choose CASIA FASD dataset, Idiap Replay Attack dataset and OULUNPU dataset for experimental verification. Experimental results show that our method is effective for face anti-spoofing task.

Citation Download Citation

Junting Chen, Sijie Niu, Xizhan Gao, Shenyuan Li, and Jiwen Dong "SA-UNet for face anti-spoofing with depth estimation", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831R (16 February 2022); https://doi.org/10.1117/12.2623137

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KEYWORDS

Image segmentation

Image analysis

Neural networks

Cameras

Convolution

Feature extraction

Computer programming

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