Anomaly Metrics on Class Variations For Face Anti-Spoofing

被引：0
|
作者：
Liu, Weihua [1 ]
Gong, Bing [1 ]
Che, Kai [2 ]
Ma, Jieming [3 ]
Pan, Yushan [3 ]
机构：
[1] School of Information Engineering, Xi’an Eurasia University, Xi’an,710000, China
[2] Research Center, Aviation Industry Corporation of China, Xi’an,710078, China
[3] School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou,215123, China
来源：
Computer Journal | 1600年 / 67卷 / 09期
关键词：
In face anti-spoofing tasks; distinguishing between live and spoof faces across different data domains presents challenges due to inter-class similarities; intra-class variations and unknown spoof patterns. This hampers generalization in real-world applications. To address this; we propose a novel convolutional neural network framework that utilizes spatial-frequency cues for 2D and 3D attacks. Furthermore; we introduce compact anomaly metrics and design three anomaly metrics-based supervisions from the perspective of Reed-Xiaoli anomaly detection; aiming to tackle the challenge posed by unknown attacks. Thanks to our proposed spatial frequency factorization network and its frequency-related supervisions; the spoofing cues are significantly enhanced; resulting in remarkable improvements in our experimental results. These outcomes demonstrate that our proposed framework achieves state-of-the-art performance on both monocular and multi-spectral benchmark datasets. © The British Computer Society 2024. All rights reserved;
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页码：2725 / 2738
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