Open AccessAn Improved Face Recognition Using Neighborhood Defined Modular Phase Congruency Based Kernel PCA
Author(s) -
M. Lokeswara Reddy,
P. Ramana Reddy
Publication year - 2012
Publication title -
international journal of electronic signal and systems
Language(s) - English
Resource type - Journals
ISSN - 2231-5969
DOI - 10.47893/ijess.2012.1053
Subject(s) - artificial intelligence , pattern recognition (psychology) , facial recognition system , principal component analysis , computer science , phase congruency , kernel (algebra) , kernel principal component analysis , feature extraction , face (sociological concept) , computer vision , feature (linguistics) , support vector machine , mathematics , kernel method , social science , linguistics , philosophy , combinatorics , sociology
A face recognition algorithm based on NMPKPCA algorithm presented in this paper. The proposed algorithm when compared with conventional Principal component analysis (PCA) algorithms has an improved recognition Rate for face images with large variations in illumination, facial expressions. In this technique, first phase congruency features are extracted from the face image so that effects due to illumination variations are avoided by considering phase component of image. Then, face images are divided into small sub images and the kernel PCA approach is applied to each of these sub images. but, dividing into small or large modules creates some problems in recognition. So a special modulation called neighborhood defined modularization approach presented in this paper, so that effects due to facial variations are avoided. Then, kernel PCA has been applied to each module to extract features. So a feature extraction technique for improving recognition accuracy of a visual image based facial recognition system presented in this paper.