Robust Low-Rank Regularized Regression for Face Recognition with Occlusion

Recently, regression analysis based classification methods are popular for robust face recognition. These methods use a pixel-based error model, which assumes that errors of pixels are independent. This assumption does not hold in the case of contiguous occlusion, where the errors are spatially correlated. Observing that occlusion in a face image generally leads to a low-rank error image, we propose a low-rank regularized regression model and use the alternating direction method of multipliers (ADMM) to solve it. We thus introduce a novel robust low-rank regularized regression (RLR³) method for face recognition with occlusion. Compared with the existing structured sparse error coding models, which perform error detection and error support separately, our method integrates error detection and error support into one regression model. Experiments on benchmark face databases demonstrate the effectiveness and robustness of our method, which outperforms state-of-the-art methods.