中文摘要：

通过提取人脸图像的Gabor特征，结合Adaboost进行人脸识别。针对Gabor特征维数高、冗余大的特点，引入Adaboost算法进行特征选择降低特征向量的维数，对于大量的Gabor特征进行选取。同时采用单一正样本集合和多个负样本集合分别进行训练的方法构建多个强分类器级联的层级分类器。在YaLe库上进行测试的结果验证了该法的有效性。

英文摘要：

Through the Gabor feature extraction of face images, face recognition is conducted with Adaboost. According to the characteristics cf the Gabor features of high dimension, redundancy, Adaboost algorithm is used to reduce the dimension of feature vectors in feature selection and select Gabor features. Hierarchical classifier while using the single positive sample set and a plurality of negative samples were training set to construct multiple classifier. Test results on YaLe face database verify the effectiveness of the method.