Advanced Face Anti-Spoofing with Depth Segmentation

Licheng Zhang1; Nan Sun2; Xihong Wu3; Dingsheng Luo3

10.1109/IJCNN55064.2022.9892826

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

2161-4393

978-1-6654-9526-4

2022 International Joint Conference on Neural Networks (IJCNN)

1-6

18-23 July 2022

Padua, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. In state-of-the-art FAS methods, face depth is determined for every position in a facial image. However, face depth varies at different positions, which leads to low accuracy when predicting face depth. As we observe, spoof faces have depth values that are 0, while live faces have depths that are equal to or greater than 0. As a result, for faces that have a depth greater than 0, if they are estimated as merely a positive value, instead of an accurate value, the prediction of whether they are real or not will not change. Further, if a range of depths are considered as one category, then there are more samples per category for the network training. Based on the above observation, in this paper, we propose to aggregate simple depth values to the same category and perform classification to optimize the FAS network. To evaluate the performance of the proposed approach, we perform extensive experiments on four benchmark databases, respectively, OULU-NPU, SiW, CASIA-FASD, and Replay-Attack. The results demonstrate that the proposed approach outperforms state-of-the-art methods on intra-database testing. Furthermore, our proposed approach shows advanced performance on cross-database testing.

Face Anti-spoofing Deep Classification Network Depth Segmentation PSPNet DeepLabv3+

第一

英语

CPCI-S

Computer Science ; Engineering ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Neurosciences

WOS:000867070907063

IEEE

1.Southern University of Science and Technology
2.University of New South Wales Canberra
3.Peking University

Licheng Zhang,Nan Sun,Xihong Wu,et al. Advanced Face Anti-Spoofing with Depth Segmentation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-6.