Structure Destruction and Content Combination for Face Anti-Spoofing
This work addresses face anti-spoofing for security systems, presenting an incremental improvement over prior methods.
The paper tackles the problem of face anti-spoofing by addressing inherent disturbances in training, such as complete facial structures and implicit subdomains, resulting in improved reliability against state-of-the-art competitors as demonstrated on extensive public datasets.
In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision. However, limited by the following two inherent disturbances in their training process: 1) Complete facial structure in a single image. 2) Implicit subdomains in the whole dataset, these methods are prone to stick on memorization of the entire training dataset and show sensitivity to nonhomologous domain distribution. In this paper, we propose Structure Destruction Module and Content Combination Module to address these two imitations separately. The former mechanism destroys images into patches to construct a non-structural input, while the latter mechanism recombines patches from different subdomains or classes into a mixup construct. Based on this splitting-and-splicing operation, Local Relation Modeling Module is further proposed to model the second-order relationship between patches. We evaluate our method on extensive public datasets and promising experimental results to demonstrate the reliability of our method against state-of-the-art competitors.