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

TL;DR

This paper introduces a new multi-modal face anti-spoofing dataset tailored for surveillance scenarios and proposes an attention-based network that improves detection accuracy on low-quality images.

Contribution

The paper presents a novel cross-domain multi-modal FAS dataset and a new attention-based network with feature augmentation for enhanced anti-spoofing performance.

Findings

Achieves state-of-the-art results on CASIA-SURF dataset.

Outperforms existing methods on the proposed GREAT-FASD-S dataset.

Effectively handles low-quality, cross-domain face anti-spoofing scenarios.

Abstract

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.