GRNet: An Efficient Group Ranking Network for Face Attribute Recognition

Face attribute recognition methods have been far from real-world applications despite remarkable progress in recent years. Most of these methods fail to mine attribute relationships with traditional cross entropy loss and hold considerable computational complexity. In this work, we propose a group ranking network (GRNet) to investigate substantial relationships among face attributes. First, an attribute grouping manner is designed to capture interactions among spatially related attributes and enhance computational efficiency. Second, a new supervision signal is presented to model attribute ranking relationships. Under the supervision of ranking loss, GRNet learns intra-attribute and inter-attribute ranking features to intensify the representational capability of attribute models. We evaluate our approach on aligned and unaligned CelebA datasets. Results show that the performance of the proposed approach is superior to other start-of-the-art methods on attribute recognition.