Structural Orthogonal Procrustes Regression for Face Recognition with Pose Variations and Misalignment

Ying Tai, Jian Yang, Fanlong Zhang, Yigong Zhang, Lei Luo, Jianjun Qian

Abstract: Regression based method is a hot topic in the face recognition community and has achieved interesting results when dealing with well-aligned frontal face images. However, most of the existing regression analysis based methods are sensitive to pose variations. In this paper, we firstly introduce the orthogonal Procrustes problem (OPP), which is simple but effective, as a model to handle pose variations in two-dimensional face images. OPP seeks an optimal transformation between two images to correct the pose from one to the other. We integrate OPP into the regression model and propose the structural orthogonal Procrustes regression (SOPR) using the nuclear norm constraint on the error term to keep image's structural information. Moreover, a subject-wise strategy is adopted to address the problem that the gallery images may span over different poses. The proposed model is solved by an efficient iteratively reweighted algorithm and experimental results on popular face databases demonstrate the effectiveness of our method.