A Complementary Regression Network for Accurate Face Alignment Abstract   This paper proposes a complementary regression network (CRN) that combines global and local regression methods to align faces. A global regression network (GRN) generates the coordinates of facial landmark points directly such that all facial feature points are fitted to the input face on the whole and a local regression network (LRN) generates the heatmap of facial landmark points such that each channel localizes the detail of its facial landmark point well. The CRN converts the GRNxe2x80x99s coordinates to another heatmap, then uses with the LRNxe2x80x99s heatmap to get the final facial landmark points. The CRN works complementarily such that the GRNxe2x80x99s overall fitting tendency compensates for the LRNxe2x80x99s poor alignment caused by missing local information, whereas the LRNxe2x80x99s detailed representation compensates for the GRNxe2x80x99s poor alignment caused by global missfitting. We conducted several experiments on the 300W public dataset, the 300W private dataset, and the Menpo dataset and the proposed CRN achieved 3.14%, 3.74%, and 1.996% thestateofart face alignment accuracy in terms of percentage of normalized mean error, respectively.

Occlusion Deep Alignment Network The performance of face localization system often degenerates when occlusions occur. In this paper, we propose Occlusion Deep Alignment Network(ODAN), which is robust to occlusion. Occlusion detection and rough face alignment are employed simultaneously, where the occlusion detection of landmarks is treated as a regression problem. Based on occlusion information, localization offset scaling is employed to transform the difference between the groundtruth and the rough landmark estimation to another linear space to generate target positions for precise face alignment, reducing the difficulty of the regression task. Finally, the outputs of the proposed model are transformed back to Euclidean Space. Experiments on two public datasets show that the proposed method achieves stateofart performance under severe occlusion both in accuracy and efficiency.