Facial Recognition Using Modified Principle Component Analysis Algorithm

A feature selection technique along with information with different expression of faces to be recognized procedure is given in this paper. Improving the recognition accuracy image-based facial recognition system is presented in this paper. A modified PCA novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and images when compared to conventional techniques.