Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction

Zhanxiong Wang, Keke He, Yanwei Fu, Rui Feng, Yu-Gang Jiang, Xiangyang Xue

Abstract: Deep neural networks have significantly improved the performance of face recognition and facial attribute prediction, which however are still very challenging on the million scale dataset, i.e. MegaFace. In this paper, we for the first time, advocate a multi-task deep neural network for jointly learning face recognition and facial attribute prediction tasks. Extensive experimental evaluation clearly demonstrates the effectiveness of our architecture. Remarkably, on the largest face recognition benchmark -- MegaFace dataset, our networks can achieve the Rank-1 identication accuracy of 77.74% and face verication accuracy 79.24% TAR at 10-6 FAR, which are the best performance on the small protocol among all the publicly released methods.