利用二元结构特征的人脸识别

提出了一种基于二元结构特征提取的人脸识别算法. 该算法将所有类进行两两组合，以两类分类器为基础，为每个两两组合类间的识别挑选最适合分类的特征构成特征选取空间. 对未知样本进行测试时，在特征选取空间中计算测试样本与所有训练类的相似度，将未知样本判断为与之相似度最大的类. 运用AT&T和AR人脸数据库对该算法进行性能测试，与其他算法相比，该算法能在较小的特征维数下获得更高的识别率.

Face Recognition Using Binary Structure-Based Feature Selection

This paper proposes a binary structure feature selection (BFS) for face recognition. In the proposed method, all classes are combined in pairs. Based on the two-class classifier, the most suitable features for discriminating these two classes are chosen to form a feature-selected space. During the test on an unknown sample image, similarities between the unknown image and all training classes are calculated in the featureselected space. The unknown image is thus judged to belong to the class which shows the highest similarity. Performance of the method has been tested with the AT&T and AR face databases. The results show that, compared with other methods, the proposed technique can achieve higher recognition rate with a low feature dimension.