Low rank driven robust facial landmark regression

Abstract Localizing facial landmarks is an essential prerequisite to facial image analysis. However, due to the large variability in expression, illumination, pose and the existence of occlusions in the real-world face images, how to localize facial landmarks more efficiently is still a challenging problem. In this paper, we present a low-rank driven regression model for robust facial landmark localization. Our approach consists of low-rank face frontalization and sparse shape constrained cascade regression steps, which lies on, (1) in terms of the low rank prior of face image, we recover such a low-rank face from its deformed image and the associated deformation despite significant distortion and corruption. Alignment of the recovered frontal face image is more simple and effective. And (2) in terms of the sparse coding of face shape on the shape dictionary learnt from training data, sparse shape constrained cascade regression model is proposed to simultaneously suppress the ambiguity in local features and outlier caused by occlusion, and sparse residual error deviated from low-rank face texture is also utilized to predict the occlusion area. Extensive results on several wild benchmarks such as COFW, LFPW and Helen demonstrate that the proposed method is robust to facial occlusions, pose variations and exaggerated facial expressions.