Face Classification Based on Natural Features and Decision Tree

Existing face recognition methods suffer from efficiency problems and heavily rely on proper features extraction. In this paper, we propose an efficient face classification method which aims to reduce sensitivity to facial variations and occlusions, meanwhile complete tasks efficiently. In contrast with most energy minimizing based recognition methods, proposed algorithm is cast as a simple classification in our method. First, preprocess images for enhancing data images prior to computational processing and label parts of images as training data. Then we use Active Shape Model (ASM) to extract robust natural features. After that we categorize features and mark them with different labels. Finally, we learn a discriminative C4.5 decision tree for classification. Our method can efficiently classify face images and robust handle facial variations and occlusions. Extensive experiments are conducted on AR database in order to demonstrate the robustness of proposed method. Quantitative and qualitative results compared with several popular algorithms suggest effectiveness and efficiency of proposed method.