基于小波分解和支持向量机的准正面人脸识别方法

基于小波分解提取人脸特征技术和多分类支持向量机模型，提出了一种新的准正面人脸识别算法。小波分解提取人脸特征具有对表情变化不敏感的特点；支持向量机作为分类器被认为具有很高的推广(generalization)性能，无需先验知识。在所提出的算法中，首先对训练图像进行预处理，然后使用小波分解方法对人脸图像进行特征提取，用所提取的人脸特征向量训练多分类支持向量机模型，最后用训练好的支持向量机进行人脸识别。利用ORL人脸图像库对该算法的实验测试结果，以及与其它人脸识别方法的比较结果表明了该算法在识别性能方面的优越性。

Quasi-Frontal Face Recognition Based on Wavelet Decomposition and Support Vector Machines

This paper presents a novel algorithm for quasi-frontal face recognition based on the wavelet decomposition technique and a multi-class Support Vector Machine (SVM) model. The extracted features from human face images by wavelet decomposition are less sensitive to the facial expression variations. The SVM is considered as a good classifier with high generalization performance with no need to add a priori knowledge. The process of the proposed method is as follows: Preprocessing the face images first, then extracting the appropriate features of human faces by wavelet decomposition, training the multi-class SVM model by the face feature vectors, and using the trained SVM model to classify the human faces at last. The ORL database of faces is selected to test and evaluate the proposed algorithm. The results of the test are encouraging and the proposed algorithm is shown to perform very well in recognition capability compared with other approaches for face recognition.