Two-Dimensional Nonlinear Discriminant Analysis and Face Recognition
Caikou Chen · Journal of Changshu Institute of Technology · 2008
Two-dimensional maximum scatter-difference discriminant analysis not only essentially avoids the small sample size problem occurred in traditional Fisher discriminant analysis,but also saves much computational time for feature extraction.In this paper,a novel two-dimensional maximum scatter difference discriminant analysis(2D-KMSDA) is developed for the extraction of nolinear feature by using the well-known kernel trick.It extracts much more effective nolinear feature and made the true recognition rate raise saliently.What's more,it also offers a unified framework for two-dimensional nolinear discriminant analysis.Finally,extensive experiments performed on AR face database verify the effectiveness of the proposed method.