Ensemble based face recognition using discriminant PCA Features

Principal Component Analysis (PCA) is one of the most widely used subspace projection technique for face recognition. In subspace methods like PCA, feature selection is fundamental to obtain better face recognition. However, the problem of finding a subset of features from a high dimensional feature set is NP-hard. Therefore, to solve the feature selection problem, heuristic methods such as evolutionary algorithms are gaining importance. In many face recognition applications, due to the small sample size (SSS) problem, it is difficult to construct a single strong classifier. Recently, ensemble learning in face recognition is gaining significance due to its ability to overcome the SSS problem. In this paper, the NP-hard problem of finding the best subset of the extracted PCA features for face recognition is solved by using the differential evolution (DE) algorithm and is referred to as FS-DE. The feature subset is obtained by maximizing the class separation in the training data. We also present an ensemble based approach for face recognition (En-FR), where different subsets of PCA features are obtained by maximizing the distance between a subset of classes of the training data instead of whole classes. The subsets of the classes are obtained by bagging and overlap each other. Each subset of the PCA features selected is used for face recognition and all the outputs are combined by a simple majority voting. The proposed algorithms, FS-DE and En-FR, are evaluated on four wellknown face databases and the performance is compared with the PCA and Fisher's LDA algorithms.