Abstract: Face recognition has advantages over other biometric methods. Principal Component Analysis (PCA) has been widely used for the face recognition algorithm. PCA has limitations such as poor discriminatory power and large computational load. Due to these limitations of the existing PCA based approach, we used a method of applying PCA on wavelet subband of the face image and two methods are proposed to select best of the eigenvectors for recognition. The proposed methods select important eigenvectors using genetic algorithm and entropy of eigenvectors. Results show that compared to traditional method of selecting top eigenvectors, proposed method gives better results with less number of eigenvectors.

Manisha Satone and G.K.Kharate, “Selection of Eigenvectors for Face Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 4(3), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040316

@article{Satone2013,
title = {Selection of Eigenvectors for Face Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040316},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040316},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {3},
author = {Manisha Satone and G.K.Kharate}
}