Title :
Enhancement of Components in ICA for Face Recognition
Author :
Lei, Jiajin ; Lu, Chao ; Pan, Zhenkuan
Author_Institution :
Welch Libr., Johns Hopkins Univ., Baltimore, MD, USA
Abstract :
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).
Keywords :
face recognition; feature extraction; image classification; image enhancement; independent component analysis; optimisation; ICA component enhancement; ICA component subspace optimization; ICA performance; face classification; face image data; face recognition system; feature extraction; independent component analysis; independent component optimization; sequential forward floating selection; Databases; Face; Face recognition; Feature extraction; Independent component analysis; Integrated circuits; Principal component analysis; Independent Component Analysis (ICA); face recognition; feature extraction;
Conference_Titel :
Software Engineering Research, Management and Applications (SERA), 2011 9th International Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4577-1028-5
DOI :
10.1109/SERA.2011.12
