Hierarchical Multi-Task Network For Race, Gender and Facial Attractiveness Recognition

Deep learning has powered many face related tasks and shown state-of-the-art performance. However, existing deep models are often trained separately for different problems, which results in heavy computational burden. To address this problem, we propose a novel multi-task network with fully convolutional architecture-Hierarchical Multi-task Network (HMT-Net), that simultaneously recognizes a person's gender, race and facial attractiveness from a given portrait image. Aiming to improve the robustness to outliers in facial beauty prediction task, a novel loss is introduced into HMTNet. Compared to existing deep approaches, the proposed HMTNet achieves state-of-the-art performance on several datasets, and it can learn more discriminative feature representation through joint training and feature aggregation. Extensive experiments evidence the effectiveness of HMTNet.