Face Verification

Face verification, the automated confirmation of a person's identity using their facial image, aims to achieve high accuracy and robustness across diverse conditions. Current research emphasizes improving performance in challenging scenarios, such as low-resolution images, varying lighting, and age progression, often employing deep learning models like Siamese networks, autoencoders, and vision transformers, along with techniques like multi-task learning and data augmentation. This field is crucial for security applications (e.g., border control, access control) and is driving advancements in areas like explainable AI and mitigating biases in facial recognition systems.