`Face recognition is one of biometrics system used for surveillance purpose. It use to discovery criminals, suspected terrorists and missed children. The face recognition is term to identify human by using algorithms. In this paper, Local Binary Pattern (LBP) approach has applied to extract features for face recognition. LBP approach is one of powerful and robust method for extraction features from face. Main contributions of this present research work are: First LBP approach used to extract importance features from face. Second: Feature selection methods, Principal Component Analysis (PCA) has employed to remove irrelevant features for increase accuracy of classification. For face recognition the most significant features are necessary due to the face has more dimensionality. Third, Classification, Two classifiers are applied for recognition purpose. Support Vector Machine (SVM) and Linear Discriminates (LD) algorithms have used to classify the features vector which has obtained by LBP method for face recognition. For experimental analysis , the Olivetti Research Laboratory (ORL ) standard data has been used to evaluate the proposed system. The empirical analysis results of proposed system show that it is better in terms of accuracy performance measures. A Comparative analysis results between the classifiers with all features and with PCA method is presented. It is observed that the performance of classifiers with whole features is better. However, the classifier with PCA method is better in cost of time. Finally, it concluded that the proposed system is more robust and better for face recognition.
Hadi et al. (Tue,) studied this question.