Facial Attribute Recognition With Feature Decoupling and Graph Convolutional Networks

Abstract

The objective of facial attribute recognition is to predict a set of labels for each face image. Similar to general multi-label image classification task, facial attribute recognition suffers from several typical problems: discriminative feature learning, handling the correlations among attributes, and imbalanced training data. In this paper, we propose a unified facial attribute recognition solution via feature decoupling and graph convolutional networks (GCN): 1) We utilize the orthonormal regularizer to constrain that each dimension of the general facial representation represents a certain visual pattern. Meanwhile, a learnable matrix is unveiled, which can convert general facial feature into attribute-specific representation through modeling the importance of different visual patterns. 2) To handle the correlations between facial attributes, we build a GCN to capture the label dependencies and map the nodes in the proposed GCN to a set of inter-dependent attribute classifiers. Besides, we normalize the weights in all classifiers to alleviate the influence of data imbalance. We have conducted extensive experiments on two benchmarks, and both the qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed method.

Keywords

• Facial attribute recognition
• graph convolutional networks
• feature representation
• multi-label learning
• attribute correlation