Face recognition based on information fusion

A method based on the fusion of global and local facial features in the framework of subspace analysis for face recognition is proposed. PCA (Principal Component Analysis) is performed to extract global features, and the results are then sent to a NN (Nearest-Neighbor) classifier for recognition. A special strategy is used to combine different local features such as eyes, eyebrows, nose and mouth according to their respective salience. The idea of FI (fuzzy integration) is adopted to fuse both global and local features and the final result is given. The experiments on the NLPR database demonstrate the effectiveness and feasibility of the proposed method.