Enhancement of Components in ICA for Face Recognition
Jiajin Lei, Chao Lu, Zhenkuan Pan · 2011
Independent Component Analysis (ICA) has found its application in face recognition successfully. The goals are to estimate the components from raw image data. These components are then used to extract features of face images on which face classification is conducted. The components play key role in face recognition system. However these separated components are not equally important in terms of contribution to the feature extraction. ICA components are un-ordered. We do not know which component is more valuable than others. In order to improve ICA performance it is highly desired to select most discriminative components that are most effective. It is of great significance for ICA face recognition to find methods for optimizing independent components (ICs). In this paper we explored two methods for this purpose. One is ICA Component Subspace Optimization, the other is Sequential Forward Floating Selection (SFFS).