Face Quality

Face quality assessment (FQA) focuses on objectively measuring the suitability of facial images for applications like face recognition, aiming to improve system accuracy and reliability. Current research emphasizes developing accurate and efficient FQA metrics, often leveraging deep learning models such as generative adversarial networks (GANs) and contrastive learning frameworks, to better align automated assessments with human perception. These advancements are crucial for enhancing the performance and robustness of face-based systems across various fields, including security, biometrics, and human-computer interaction, while also addressing biases inherent in existing methods.