Selection of distinctive SIFT feature based on its distribution on feature space and local classifier for face recognition

This paper investigates a face recognition system based on SIFT (Scale Invariant Feature Transform) feature and its distribution on feature space. The system takes advantage of SIFT which possess strong robustness to expression, accessory pose and illumination variations. Since we use each of SIFT keypoint as the feature of face and SIFT keypoints are very complicated in feature space, we apply the feature partition on SOM (Self Organizing Map) and adopt local MLP (Multilayer Perceptron) for each node on map to improve the classification performance. Moreover the distinctive features from all SIFT keypoints in each face class are defined and extracted based on feature distribution on SOM. Finally the face can be recognized through the proposed scoring method depending on the classification result of these distinctive features. In the experiments, the proposed method gave a higher face recognition rate than other methods including matching and holistic feature based methods in three famous databases.