Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis

Principal component analysis (PCA) is widely applied in various areas, one of the typical applications is in face. Many versions of PCA have been developed for face recognition. However, most of these approaches are sensitive to grossly corrupted entries in a 2D matrix representing a face image. In this paper, we try to reduce the influence of grosses like variations in lighting, facial expressions and occlusions to improve the robustness of PCA. In order to achieve this goal, we present a simple but effective unsupervised preprocessing method, two-dimensional whitening reconstruction (TWR), which includes two stages: 1) A whitening process on a 2D face image matrix rather than a concatenated 1D vector; 2) 2D face image matrix reconstruction. TWR reduces the pixel redundancy of the internal image, meanwhile maintains important intrinsic features. In this way, negative effects introduced by gross-like variations are greatly reduced. Furthermore, the face image with TWR preprocessing could be approximate to a Gaussian signal, on which PCA is more effective. Experiments on benchmark face databases demonstrate that the proposed method could significantly improve the robustness of PCA methods on classification and clustering, especially for the faces with severe illumination changes.