Improving Multiview Face Detection with Multi-Task Deep Convolutional Neural Networks

Multiview face detection is a challenging problem due to dramatic appearance changes under various pose, il-lumination and expression conditions. In this paper, we present a multi-task deep learning scheme to enhance the detection performance. More specifically, we build a deep convolutional neural network that can simultaneously learn the face/nonface decision, the face pose estimation prob-lem, and the facial landmark localization problem. We show that such a multi-task learning scheme can further improve the classifier’s accuracy. On the challenging FDDB data set, our detector achieves over 3 % improvement in detec-tion rate at the same false positive rate compared with other state-of-the-art methods. 1