Face recognition based on texture features and two stage classifier
LI Zhaogu · Computer Engineering and Applications Journal · 2013
In order to improve the face recognition rate and recognition efficiency, this paper proposes a new face recognition model based on texture feature and two class classifier combination. Texture features are extracted by gray level co-occurrence matrix, and the Euclidean distance between face image and template, and then the rejection criteria is used for evaluation. If the face image category is clearly, Euclidean distance classifier is used to identify the face, otherwise face image is recognized by SVM classifier. The simulation experiment is carried out on the ORL face database and Yale face database. The simulation results show that, compared with the single classifier, the proposed classifier not only has improved the recognition efficiency, but also improved the rate of face recognition. It has better face recognition performance.