A Novel High-Performance Face Anti-Spoofing Detection Method

Authors- Srinath S, Assistant Professor Dr. Krithika. D. R

Abstract-– Existing face anti-spoofing models using deep learning for multi-modality data suffer from low generalization when using a variety of presentation attacks such as 2D printing and high-precision 3D face masks. One of the main reasons is that the non-linearity of multi-spectral information used to preserve the intrinsic attributes between a real and a fake face is not well extracted. To address this issue, we propose a multi-ability data-based two-stage cascade framework for face anti-spoofing. The proposed framework has two advantages. Firstly, we design a two-stage cascade architecture to selectively fuse low-level and high-level features from different modalities to improve feature representation. Secondly, we use multi-modality data to construct a distance-free spectral on RGB and infrared (IR) to augment the non-linearity of data. The presented data fusion strategy is different from popular fusion approaches, since it can strengthen the discrimination ability of network models on physical attribute features rather than identity structure features under certain constraints. In addition, a multi-scale patch-based weighted fine-tuning strategy is designed to learn each specific local face region. Experimental results show that. The proposed framework achieves better performance than other state-of-the-art methods on both benchmark datasets and self-established datasets, especially on multi-material mask spoofing.

DOI: /10.61463/ijset.vol.13.issue2.401