A Novel Efficient Face Recognition Using Two Level Evolution Classifier

We present a new efficient face recognition using two level adaptive classifier scheme. Face recognition is a very popular problem in computer vision area. However, the maturity of face recognition technology still need much progress since its performance is depending on several varying factors such as facial illumination, expression, pose etc. In this paper, we propose an adaptive classifier in order to reduce effect of facial feature variations due to the change of facial expressions. Facial features are eyes, nose, and mouth feature groups each of which is constituted of facial feature points. All facial images are treated by first initial evolutionary classifier using initial feature group level and feature point level parameter set. All rejected facial images based on threshold are employed for generating new feature parameter set using two level chromosome evolutions. The feature space adopted here is Gabor wavelet with weight Gabor parameters. We have performed extensive experiments using international standard FERET face image dataset and we have achieved very encouraging results.