纹理特征和两级分类器相结合的人脸识别

为了提高人脸识别率和识别效率，提出一种纹理特征和两级分类器相结合的人脸识别方法。采用灰度共生矩阵表示人脸图像的纹理特征，计算待识别人脸图像与模板间欧式距离，采用拒识阈值进行评判，如果人脸图像归属类别清楚，则采用欧式距离分类器进行识别，否则将待识人脸图像送入SVM分类器进行识别，采用ORL人脸数据库和Yale人脸数据库进行仿真实验。仿真结果表明，相对于单一人脸识别器，两级分类器不仅提高了人脸识别效率，而且提高了人脸识别率，具有更好的人脸识别性能。

Face recognition based on texture features and two stage classifier

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.