Joint multi-feature fusion and attribute relationships for facial attribute prediction
Pingyu Wang, Fei Su, Zhicheng Zhao · 2017
Predicting facial attributes from wild images is very challenging due to complex face variations. The key to this problem is to construct rich facial representations and take advantage of attribute relationships. In this paper, we propose a novel multi-task convolutional neural network (MTCNN) and a supervision signal called Online Batch Relation Loss (OBRL) for face attribute prediction in the wild. In particular, MTCNN builds informative facial features by embedding identity, age and race features from IdentityNet, AgeNet and RaceNet respectively. In addition, OBRL can diminish distribution shift of attribute relationships by mining attribute correlation within each minibatch, while it penalizes the probability divergence between a pair of attributes. In order to learn discriminative attribute features, we feed AttributeNet with fused facial features and partition attributes into nine groups to share intra-group features and reduce redundant computation. Finally, AttributeNet is optimized with the joint supervision of Cross Entropy Loss and OBRL. Experiments on CelebA and LFWA show that the proposed method outperforms the state-of-the-art methods with a significant margin.