Joint Head Pose Estimation with Multi-task Cascaded Convolutional Networks for Face Alignment

In the past decades, face alignment has been studied widely, but it has long been impeded by the problem of pose variation. Recent studies show that pose information used as additional source of information can help address the above problem. In this paper, we adopt a multi-task cascaded CNNs based framework for simultaneous face detection, dense face alignment and fine head pose estimation. Especially, our framework exploits the inherent correlation between face alignment and fine head pose estimation to boost up landmark detection robustness in the case of various poses. Experiments show that our method not only demonstrates real-time performance for face detection, dense face alignment and fine head pose estimation, but also outperforms most state-of-the-art methods for face alignment on the challenging 300-W benchmark. Especially in the case of large pose variations, it achieves outstanding results.