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2011 Ninth International Conference on Software Engineering Research, Management and Applications
Enhancement of Components in ICA for Face Recognition
Baltimore, Maryland USA
August 10-August 12
ISBN: 978-0-7695-4490-8
| ASCII Text | x | ||
| Jiajin Lei, Chao Lu, Zhenkuan Pan, "Enhancement of Components in ICA for Face Recognition," Software Engineering Research, Management and Applications, ACIS International Conference on, pp. 33-38, 2011 Ninth International Conference on Software Engineering Research, Management and Applications, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/SERA.2011.12, author = {Jiajin Lei and Chao Lu and Zhenkuan Pan}, title = {Enhancement of Components in ICA for Face Recognition}, journal ={Software Engineering Research, Management and Applications, ACIS International Conference on}, volume = {0}, year = {2011}, isbn = {978-0-7695-4490-8}, pages = {33-38}, doi = {http://doi.ieeecomputersociety.org/10.1109/SERA.2011.12}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Software Engineering Research, Management and Applications, ACIS International Conference on TI - Enhancement of Components in ICA for Face Recognition SN - 978-0-7695-4490-8 SP33 EP38 A1 - Jiajin Lei, A1 - Chao Lu, A1 - Zhenkuan Pan, PY - 2011 KW - face recognition KW - Independent Component Analysis (ICA) KW - feature extraction VL - 0 JA - Software Engineering Research, Management and Applications, ACIS International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SERA.2011.12
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).
Index Terms:
face recognition, Independent Component Analysis (ICA), feature extraction
Citation:
Jiajin Lei, Chao Lu, Zhenkuan Pan, "Enhancement of Components in ICA for Face Recognition," sera, pp.33-38, 2011 Ninth International Conference on Software Engineering Research, Management and Applications, 2011
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