Earticle

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

저자

• 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 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605