Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

TL;DR

This paper introduces a novel deep ensemble learning approach with frame skipping to improve face anti-spoofing by efficiently capturing motion patterns, achieving state-of-the-art results across multiple datasets.

Contribution

It proposes a frame skipping mechanism combined with ensemble RNNs and a meta-model for enhanced face anti-spoofing detection performance.

Findings

Achieved state-of-the-art HTERs on MSU-MFSD, Replay-Attack, and OULU-NPU datasets.

Effective motion prediction through frame skipping improves spoofing detection.

Ensemble of RNNs with a meta-model enhances overall accuracy.

Abstract

Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario.