Algorithm Fusing Gabor Feature Based on Random Dot Product Graph
In order to improve face recognition accuracy by using random graph theory,this paper presents a algorithm fusing Gabor feature based on random dot product graph. First use Gabor filter to extract Gabor feature from the training set. In order to reduce data redundancy,these features require some pre-treatment. Then access node assignment by reconstructing the training set of face images with random graph. According to the cosine of the assignment vector angle and their category information,calculated penalty value for each node. The identification image is projected to the subspaces which is reconstructed by the random dot product graph,classified based on the nearest neighbor node's penalty value. Experimentation on the ORL face image database indicate the effectiveness of the algorithm.