利用小波分解提取人脸特征技术和支持向量机 (SVM)分类模型，提出了一种基于个人身份认证的正面人脸识别算法 (或称为人脸认证方法 ).针对 M个用户的人脸认证算法包括二个阶段 :(1)训练阶段 :使用小波分解方法对脸像训练集中的人脸图象进行特征提取，并用所提取的人脸特征向量训练 M个 SVM(对应 M个用户 ) ;(2 )认证阶段 :先由待认证者所声称的用户身份 (姓名或密码等 )确定对应的一训练好的 SVM,然后用这一 SVM对小波分解方法提取的待认证人的脸像特征向量进行分类，分类结果将显示待认证人所声称的身份是否真实.利用 ORL人脸图象库对该算法的实验测试结果，以及与径向基函数神经网络作为分类器时的实验结果比较表明了该算法性能的优越性

This paper presents an effective algorithm for frontal face verification based on the wavelet decomposition technique and Support Vector Machines (SVMs). The process of the proposed method for face verification of M clients consists of two stages.(1) Training stage: by the wavelet decomposition, extracting the appropriate features from the facial images in the prepared training database of faces, training M SVMs for the M clients by the extracted facial feature vectors. (2) Verification stage: selecting a trained SVM from the M SVMs based on the identity claim (such as a name or a password) of an unknown person and using the trained SVM to classify the facial feature vector extracted from the facial image of the unknown person by the wavelet decomposition, and the classification result will show whether or not the identity claim of the unknown person is valid. The ORL database of faces is selected to test and evaluate the proposed algorithm. The results of the test are encouraging and the SVMs in the proposed algorithm are shown to perform very well in classification capability when compared to the traditional radial-basis function networks applied as classifiers in the algorithm.