Skip to Main Content
A new face recognition algorithm based on image fusion is presented in this paper. Each original image sample is divided into a certain number of subimages and all training subimages from the same position construct a series of new training sub-pattern sets where Principal Component Analysis (PCA) method is used to extract local projection sub-feature vectors separately, then a set of projection sub-spaces can be obtained. For improving the robustness of face recognition against illumination, the face feature was extracted using the Gabor wavelet. To an unknown face image, after the same partition, projected subfeature vectors of corresponding sub-space are gained. After classification of local projected subfeatures, strategy of synthetic is adopted to fuse each of them. At last the result of classification is determined by maximum membership principle. Simulation experiments indicate that the proposed scheme does can suitably fuse local sub-feature of face images, improve recognition rate effectively and robust.