Earticle

초록

영어

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) coined Kernel Weighted Scatter Two Dimensional Discriminant Analysis (KWS2DDA). The projection is performed through 2-direction which simultaneously works in row and column directions to solve the small sample size problem. This nonlinear dimensionality reduction algorithm has several interesting characteristics. It’s overcomes the singularity problem, while achieving efficiency. In order to improve the performance of the proposed algorithm, we introduce Gaussian RBF kernel functions. We have performed multiple face recognition experiments to compare KWS2DDA with other dimensionality reduction methods showing that KWS2DDA consistently gives the best result than the other method.

목차

Abstract
 1. Introduction
 2. Two Dimensional Linear Discriminant Analysis (2DLDA)
 3. Weighted Scatter Difference Discriminant Analysis
 4. Analysis Method
 5. Kernel Weight Scatter Two Dimensional Discriminant Analysis (KWS2DDA)
 6. Experiment and Results
  6.1. The Experiments on the ORL Face Base
  6.2 Experiment on the Yale Database
  6.3 Experiment on the Head Pose Database
 7. Conclusion
 References

키워드

저자

• Hythem Ahmed [ Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University ]
• Mohamed Jedra [ Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University ]
• Nouredine Zahid [ Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University ]

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