A Dual Approach with CycleGANs-based Face Reconstruction and ViT-based Classification

Facial recognition technology plays a crucial role in various domains, including security, surveillance, and biometric authentication. However, the accuracy of conventional facial recognition systems is significantly compromised in degraded conditions, characterized by head pose variations, occlusions, and variations in lighting. In this study, we propose an innovative approach to address this challenge by integrating advanced deep-learning techniques. Specifically, we leverage Cycle Generative Adversarial Networks (CycleGANs) for facial reconstruction and vision Transformers (ViT) for recognition in degraded conditions. GANs are employed to enhance the quality of degraded facial images by generating high-resolution reconstructions from low-quality inputs. The reconstructed images are then fed into Transformer-based models, which extract discriminative features for robust recognition. To evaluate the effectiveness of our approach, we conducted extensive experiments using publicly available datasets of degraded facial images: the EURECOM Kinect Face Dataset and the IST-EURECOM Light Field Face Database. Our results demonstrate significant improvements in face recognition accuracy compared to traditional methods, particularly in scenarios with low-resolution or occluded faces. Furthermore, we analyze the computational efficiency and scalability of our proposed approach, highlighting its potential for real-world deployment.