Adaptive noise dictionary construction via IRRPCA for face recognition

Recently, regression analysis has become a popular method for face recognition. Various robust regression methods have been proposed to handle with different recognition tasks. In this paper, we attempt to achieve this goal by the strategy of adding an adaptive noise dictionary (AND) to the training samples. In contrast to the previous methods, the noise dictionary (ND) is adaptive to different kinds of noise and extracted automatically. To get an effective noise dictionary, the Iteratively Reweighted Robust Principal Component Analysis (IRRPCA) is proposed. A corresponding classifier based on linear regression is presented for recognition. As this adaptive noise dictionary can describe the noise distribution of testing samples, it is robust to various kinds of noise and applicable for recognition tasks with occluded or corrupted images. This method is also extended to deal with misaligned images. Experiments are conducted on AR, Yale B, CMU PIE, CMU Multi-Pie, LFW and Pubfig databases to verify the robustness of our method to variations in occlusion, corruption, illumination, misalignment, etc.