General-to-specific learning for facial attribute classification in the wild

Recent studies have shown that facial attributes provide useful cues for a number of applications such as face verification. However, accurate facial attribute interpretation is still a formidable challenge in real life due to large head poses, occlusion and illumination variations. In this work, we propose a general-to-specific deep convolutional network architecture for predicting multiple attributes from a single image in the wild. First, we model the interdependencies among all attributes by joint learning them all. Second, task-aware learning is adopted to explore the disparity regarding each attribute. Finally, an attribute-aware face cropping scheme is proposed to extract more discriminative features from where a certain attribute naturally shows up. The proposed learning strategy ensures both robustness and performance of our model. Extensive experiments on two challenging publicly available datasets demonstrate the effectiveness of our architecture and the superiority to state-of-the-art alternatives.