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

Yubao Sun; Bin Hu; Jiankang Deng; Xing Li

doi:10.1117/12.2178787

4 March 2015 Supervised descent method with low rank and sparsity constraints for robust face alignment

Yubao Sun, Bin Hu, Jiankang Deng, Xing Li

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 944304 (2015) https://doi.org/10.1117/12.2178787
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

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

Citation Download Citation

Yubao Sun, Bin Hu, Jiankang Deng, and Xing Li "Supervised descent method with low rank and sparsity constraints for robust face alignment", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944304 (4 March 2015); https://doi.org/10.1117/12.2178787

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