Robust Face Recognition Based on an Angle-Aware Loss and Masked Autoencoder Pre-Training

Abstract

Despite the advances in deep learning techniques, accurate identification using face recognition (FR) systems remains challenging owing to changes in face angles, bad lighting, and occlusions. To address these problems, we propose an optimized approach to improve the robustness of feature extraction models that are used in FR systems. The proposed method leverages an angle-aware loss function, inspired by ArcFace, that provides a large margin for significantly rotated faces. Additionally, a pre-trained weight initialization was derived from a masked autoencoder to enhance the ability of the model to cope with various poor conditions. The experimental results indicate that the proposed method outperforms existing face recognition methods in both normal and adverse environments.