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논문검색

Sparse Kernel Principal Components Analysis for Face Recognition in RGB Spaces

원문정보

Minghai Xin, Yang Liu, Jingjie Yan

보안공학연구지원센터(IJHIT) International Journal of Hybrid Information Technology Vol.7 No.2 2014.03 pp.217-226
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초록

영어

This paper presents a kinds of information fusion algorithm based on multi-channel color image. The color face image is first separated into three pseudo grayscale images: R, G, and B, then the partial characteristics of face is extracted by use of Gabor wavelet transform from each component to be eigenvector in series connection, which will be through dimensionality reduction by sparse kernel principal components to be recognized and classified by the nearest classifier. In order to testify the validity, we make experiment by use of XM2VTS color face dataset and the experimental result supports the proposed method.

목차

Abstract
 1. Introduction
 2. KPCA Algorithm Thought
 3. Sparse Kernel Principal Components Analysis
 4. Sparse Kernel Principal Components Analysis for Color Face Recognition
 5. Experimental
 6. Summary
 Acknowledgements
 References

키워드

저자정보

  • Minghai Xin Research Centre for Learning Science, Southeast University, Nanjing, China, 210096, School of Computer Science and Technology, Huaqiao University, Xiamen 361021
  • Yang Liu Research Centre for Learning Science, Southeast University, Nanjing, China, 210096
  • Jingjie Yan Research Centre for Learning Science, Southeast University, Nanjing, China, 210096

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자료제공 : 네이버학술정보

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