Semi-Supervised learning for Face Anti-Spoofing using Apex frame
Usman Muhammad Mourad Oussalah Jorma Laaksonen
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.