ASD: Towards Attribute Spatial Decomposition for Prior-Free Facial Attribute Recognition

• Published in: Proceedings (IEEE International Conference on Multimedia and Expo) pp. 1 - 9
• Main Authors: Hu, Chuanfei, Shao, Hang, Dong, Bo, Wang, Zhe, Wang, Yongxiong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.07.2024
• Subjects: attribute decomposition; Codes; Correlation; deep learning; Face recognition; Facial attribute recognition; Matrix decomposition; Minimization; prior-free method; Reliability; Spatial diversity
• ISSN: 1945-788X
• DOI: 10.1109/ICME57554.2024.10688212

Representing the spatial properties of facial attributes is a vital challenge for facial attribute recognition (FAR). Recent advances have achieved the reliable performances for FAR, benefiting from the description of spatial properties via extra prior information. However, the extra prior information might not be always available, resulting in the restricted application scenario of the prior-based methods. Meanwhile, the spatial ambiguity of facial attributes caused by inherent spatial diversities of facial parts is ignored. To address these issues, we propose a prior-free method for attribute spatial decomposition (ASD), mitigating the spatial ambiguity of facial attributes. The attribute components could be formally described in terms of the spatial locations without any extra prior information. Experimental results demonstrate the superiority of ASD compared with state-of-the-art prior-based methods on both CelebA and LFWA.