Facial landmark detection via pose-induced auto-encoder networks

Facial landmark detection is an important issue in a face recognition system. However, human faces in wild conditions often present large variations in shape due to different poses, occlusions or expressions, which makes it a difficult task. Instead of learning the same map for all images, we propose a Pose-Induced Auto-encoder Networks (PIAN) approach which uses different pose-induced networks for landmark estimation under different pose conditions. Firstly, we build a network to simultaneously get the initial landmark and pose estimation. Then, different networks which are induced by the estimated pose are built for local search where a component-based searching method is explored. By using the pose inducing strategy, the initial estimation is reliable and it helps reduce the variations in local patches. This makes the component-based search feasible and more accurate than previous searching methods. Experiments show that our method outperforms the state-of-the-art algorithms especially in terms of fine estimation of landmarks.