Transforming Challenges: Siamese-Based Vision Transformers for Robust Occluded Face Recognition

Face recognition systems are essential in various applications. Still, dealing with deteriorated situations such as fluctuations in head posture, lighting, facial expressions, and partial occlusion, presents great difficulty for them. In this work, we provide a novel method for reliable face identification under challenging circumstances by leveraging the Siamese network-based vision transformer architecture. The Siamese network is known for its ability to learn powerful representations from pairs of input data, making it suitable for handling complex variations in face images. We introduce a Transformer-based architecture that integrates Siamese networks to capture long-range dependencies and spatial relationships in facial features effectively. Our method focuses on learning discriminative features from degraded face images, enabling accurate recognition even in challenging conditions. According to experimental findings, our suggested approach works better than current practices in recognizing faces under various degradation factors. The proposed Siamese network-based transformer shows promising results on the two publicly available datasets the EKFD and the IST-EURECOM LFFD offering a reliable solution for face recognition in real-world scenarios with degraded conditions.