Pose-Robust and Discriminative Feature Representation by Multi-task Deep Learning for Multi-view Face Recognition

Automatic face recognition (FR) under uncontrolled environments has attracted considerable research attention. In the uncontrolled environments, pose variation is known as one of the crucial factors that influences FR performance. In this paper, we propose a discriminative and pose-robust feature representation using the multi-task learning in deep convolutional neural networks (ConvNet). We introduce four tasks (i.e., maximizing inter-class variation, minimizing intraclass variation, minimizing intra-pose variation, and preserving pose continuity) to learn the ConvNet. Moreover, two-stage learning strategy is proposed to minimize the error functions in learning the deep ConvNet. The extensive experimental results (with the challenging CMU MultiPIE dataset containing pose variations) show that the proposed method outperform stateof-the-art in terms of FR accuracy. Furthermore, the proposed method shows significant improvement even for the face images whose poses are not included in training set.