원문정보
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
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