mlsanityData to 7 Oct 2026

A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios

arXiv 2103.15409 ↗First posted 29 Mar 2021cs.CVcited by 1

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Abstract (arXiv)

Face Anti-spoofing (FAS) is a challenging problem due to complex serving scenarios and diverse face presentation attack patterns. Especially when captured images are low-resolution, blurry, and coming from different domains, the performance of FAS will degrade significantly. The existing multi-modal FAS datasets rarely pay attention to the cross-domain problems under deployment scenarios, which is not conducive to the study of model performance. To solve these problems, we explore the fine-grained differences between multi-modal cameras and construct a cross-domain multi-modal FAS dataset under surveillance scenarios called GREAT-FASD-S. Besides, we propose an Attention based Face Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards low-quality face images. It consists of the depthwise separable attention module (DAM) and the multi-modal based feature augment module (MFAM). Our model can achieve state-of-the-art performance on the CASIA-SURF dataset and our proposed GREAT-FASD-S dataset.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 62 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

26 of 62 references.

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14 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Surveillance Face Anti-spoofingcited by 12