Facial feature parsing and landmark detection via low-rank matrix decomposition

Facial feature parsing is an active research topic in image understanding. In this paper, we address the feature parsing problem by applying low-rank matrix decomposition on facial images. Specifically, the face is considered as a combination of the skin background which resides in a low dimensional subspace with the salient feature components (e.g., eyes, nose and mouth) as the sparse noise. Given a face image, the feature parsing problem can then be naturally formulated as sparse noise detection when recovering a low-rank matrix. To enhance the feature parsing, a linear transformation matrix is learned to boost the discriminant feature extraction. Furthermore, with the derived parsing maps, the algorithm is easily extended to implement facial landmark detection task. The effectiveness of the proposed algorithm is evaluated through several experiments on comprehensive datasets.