Face feature selection using genetic algorithm under different biometric variations

In the current study face recognition under different biometric variations is investigated applying Principal Components Analysis (PCA). In order to improve the recognition performance Genetic Algorithm (GA) is selected. The algorithm follows optimized selection of PCA features based on GA operations on the datasets ORL, FERET and BANCA. The maximum recognition rate (MRR) results obtained with ORL and FERET databases are found to be close to the results of computed with WAVELET-PCA-GA-SVM method. Further the MRR results obtained for BANCA database is 100% as that of the computed with WAVELET-PCA-GA-SVM method for YALE and YALE-B databases. Generally PCA on GA is found to be effective in removing irrelevant data groups and therefore it improves the performance.