DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

TL;DR

This paper introduces a deep reinforcement learning framework combining CNN and RNN for face anti-spoofing, inspired by human visual behavior, achieving state-of-the-art results on public datasets.

Contribution

It proposes a novel face anti-spoofing framework that models local and global feature exploration using deep reinforcement learning and recurrent mechanisms.

Findings

Achieves state-of-the-art performance on multiple public datasets.

Effectively models local and global face features for anti-spoofing.

Demonstrates robustness through extensive experiments and visualization.

Abstract

Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i.e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). In particular, we model the behavior of exploring face-spoofing-related information from image sub-patches by leveraging deep reinforcement learning. We further introduce a recurrent mechanism to learn representations of local information sequentially from the explored sub-patches with an RNN. Finally, for the classification purpose, we fuse the local information with the global one, which can be learned from the original input image through a CNN. Moreover, we conduct extensive experiments, including ablation study and visualization analysis, to evaluate our proposed framework on various public databases. The experiment results show that our method can generally achieve state-of-the-art performance among all scenarios, demonstrating its effectiveness.