Supervised descent method with low rank and sparsity constraints for robust face alignment

Abstract

Supervised Descent Method (SDM) learns the descent directions of nonlinear least square objective in a supervised manner, which has been efficiently used for face alignment. However, SDM still may fail in the cases of partial occlusions and serious pose variations. To deal with this issue, we present a new method for robust face alignment by utilizing the low rank prior of human face and enforcing sparse structure of the descent directions. Our approach consists of low rank face frontalization and sparse descent steps. Firstly, 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. Then, we propose a sparsity regularized supervised descent model by enforcing the sparse structure of the descent directions under the l1constraint, which makes the model more effective in computation and robust to partial occlusion. Extensive results on several benchmarks demonstrate that the proposed method is robust to facial occlusions and pose variations