Pre Trained Face Recognition

Pre-trained face recognition leverages deep learning models trained on massive datasets to perform facial identification and verification tasks. Current research emphasizes improving fairness and robustness by addressing demographic biases through score normalization techniques and developing modality-agnostic systems capable of handling diverse image types (e.g., visible light, thermal, sketches). These advancements are crucial for mitigating potential societal biases in applications like law enforcement and security, while also enhancing the reliability and generalizability of face recognition technology across various real-world scenarios. Furthermore, research focuses on improving the quality assessment of input images and disentangling factors like pose and identity to improve accuracy and reduce error rates.