Fuzzy Kernel Two-dimensional Principal Component Analysis for Face Recognition

Abstract

-The traditional kernel two-dimensional principal component analysis (K2DPCA) method did not take full advantage of the class information for face images and there are both "outer class" problem and "hard classifier" problem on face recognition. Therefore, a new face recognition method based on fuzzy kernel two-dimensional principal component analysis (FK2DPCA) is presented . Firstly, it introduces fuzzy concept into K2DPCA. Secondly, the class separability of criterion will be extended to high dimensional feature space by the use of kernel method. Furthermore, we select the eigenvectors that betweenclass scatter is greater than within-class scatter after projection as optimal projection axis. Finally, it uses the nearest neighbor classifier for face recognition . The experiment results on ORL and YALE face databases show that the FK2DPCA is better than other traditional methods.

A review on recent techniques in multimodal biometrics

“…This method first transforms the large dimensional data into space F through non linear mapping and then linear PCA is applied to reduce the dimension. Standard kernel functions would not provide good result on the data set like Swiss role [54].The kernel function may be combined with Fuzzy logic functions.…”