Multi-task Learning for Face Recognition and Attribute Estimation

Convolution neural network (CNN) has been shown as the state-of-the-art approach for learning face representations in recent years. However, previous works only utilized identity information instead of leveraging human attributes (e.g., gender and age) which contain high-level semantic meanings to learn robuster features. In this work, we aim to learn discriminative features to improve face recognition through multi-task learning with human attributes. Specifically, we focus on simultaneously optimizing face recognition and human attributes estimation. In our experiments, we learn face representation by training the largest publicly face dataset CASIA-WebFace with gender and age label, and then evaluate learned features on widely-used LFW benchmark for face verification and identification. We also compare the effectiveness of different attributes for identification. The results show that the proposed model outperforms hand-crafted feature such as high-dimensional LBP, and human attributes really provide useful semantic cues. We also do experiments on gender and age estimation on Adience benchmark to justify that human attribute prediction can also benefit from rich identity information.