Face Recognition Based on Multi-view Ensemble Learning

Face recognition is an important research area in human-computer. To solve the problem about the inaccuracy and incompleteness of feature extraction and recognition, an ensemble learning method on face recognition is proposed in this paper. This method is a combination of a variety of feature extraction and classification ensemble technology. In feature extraction, wavelet transform and edge detection are used for extracting features. In classification recognition, the K nearest neighbor (KNN) classifier, wavelet neural network (WNN) and support vector machine (SVM) are used for preliminary identification. Each classifier corresponds to a feature method and then the classification of the three views are constructed. The final output results are integrated by voting strategy. Experimental results show that this method can improve the identification rate compared with the single classifier.