Earticle

초록

영어

Subspace learning is an important direction in computer vision research. In this paper, a new method of face recognition based on uncorrelated multilinear principal component analysis (UMPCA) and linear discriminant analysis (LDA) is proposed. First, instead of transforming matrices into vectors for principal component analysis (PCA), UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. A subset of features is extracted and the classical LDA is then applied to find the best subspaces. Finally, the comprehensive experiments are provided on AT&T databases and the experiment results show its superiority through the comparison with other PCA plus LDA based algorithms.

목차

Abstract
 1. Introduction
 2. Tensor Fundamentals
  2.2. Tensor-to-Vector Projection
 3. Uncorrelated Multilinear Principal Component Analysis and Linear Discriminant Analysis
  3.1. Uncorrelated Multilinear Principal Component Analysis
  3.2. Linear Discriminant Analysis
 4. Experiment Evaluation
  4.1. The AT&T Database
  4.2. The FERET Database
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Fan Zhang [ School of Information Engineering, Zhengzhou University, Zhengzhou 450000, P. R. China, School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450000, P. R. China ]
• Lin Qi [ School of Information Engineering, Zhengzhou University, Zhengzhou 450000, P. R. China, School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450000, P. R. China ]
• Enqing Chen [ School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450000, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605