The Good, the Better, and the Best: Improving the Discriminability of Face Embeddings through Attribute-aware Learning

ArXi:2603.15062v1 Announce Type: new Despite recent advances in face recognition, robust performance remains challenging under large variations in age, pose, and occlusion. A common strategy to address these issues is to guide representation learning with auxiliary supervision from facial attributes, encouraging the visual encoder to focus on identity-relevant regions. However, existing approaches typically rely on heterogeneous and fixed sets of attributes, implicitly assuming equal relevance across attributes.