Semi-Supervised learning for Face Anti-Spoofing using Apex frame

TL;DR

This paper introduces a semi-supervised face anti-spoofing method that uses Gaussian-weighted apex frames to capture key video moments, improving detection accuracy across multiple datasets.

Contribution

It proposes a novel apex frame generation technique using Gaussian weighting and leverages semi-supervised learning with unlabeled apex frames for enhanced face anti-spoofing.

Findings

Effective in multiple face anti-spoofing datasets

Improves discrimination between live and spoof videos

Utilizes both labeled and unlabeled apex frames

Abstract

Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance. However, identifying the optimal frames that provide the most valuable input for the face anti-spoofing remains a challenging task. In this paper, we address this challenge by employing Gaussian weighting to create apex frames for videos. Specifically, an apex frame is derived from a video by computing a weighted sum of its frames, where the weights are determined using a Gaussian distribution centered around the video's central frame. Furthermore, we explore various temporal lengths to produce multiple unlabeled apex frames using a Gaussian function, without the need for convolution. By doing so, we leverage the benefits of semi-supervised learning, which considers both labeled and unlabeled apex frames to effectively discriminate between live and spoof classes. Our key contribution emphasizes the apex frame's capacity to represent the most significant moments in the video, while unlabeled apex frames facilitate efficient semi-supervised learning, as they enable the model to learn from videos of varying temporal lengths. Experimental results using four face anti-spoofing databases: CASIA, REPLAY-ATTACK, OULU-NPU, and MSU-MFSD demonstrate the apex frame's efficacy in advancing face anti-spoofing techniques.