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| Feature Extraction Based on Symmetrical ICA and Its Application to Face Recognition |
| ZHENG YuJie1, YANG JingYu1, WU XiaoJun2,3, YU DongJun1 |
1.Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
2.School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003
3.Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110015 |
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Abstract Independent Component Analysis (ICA) has been extensively used in the field of signal processing and image processing. In this paper, a new algorithm called symmetrical ICA (SICA) based on facial symmetry is proposed. This algorithm is based on the theory of function decomposition in algebra and mirror symmetry in geometry. In this algorithm, mirror transform is firstly introduced. Then, even/odd symmetrical samples are produced based on the theory of the even/odd decomposition principle, and the even/odd independent components are extracted from the corresponding samples respectively. Both theoretical analysis and experimental results demonstrate that this algorithm not only enlarges the number of training samples, but also remarkably raises the recognition rate. Experiment results also show this algorithm is not sensitive to the illumination variation of human faces.
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Received: 25 October 2004
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2006, Vol. 19 
