小波变换和特征加权融合的人脸识别

在人脸识别领域,提取人脸特征和降低维数是人脸识别的关键。传统的基于小波变换的人脸识别算法仅在小波分解的低频分量上提取用于分类的图像特征,造成了高频分量中部分对识别有利信息的丢失。为了更有效地提取人脸图像特征,提出一种基于小波变换和特征加权融合的人脸识别算法。首先通过小波变换对人脸图像进行降维处理,然后对4个小波子图分别运用主成分分析法(PCA)提取特征,并把这4部分特征加权融合,最后利用支持向量机(SVM)进行分类识别。在ORL人脸库上进行实验验证,识别准确率可达到97.5%,实验结果表明该算法能够有效提高人脸识别能力,与传统识别算法相比具有较高的识别准确率和识别速度。

Face recognition based on wavelet transform and weighted fusion of face features

Obtain appropriate low-dimension face features is an important problem in the area of face recognition. Traditional face recognition algorithms based on wavelet transform extract image features using only the low frequency components for classification, which results in the loss of information,which could be used for face recognition. In order to effectively extract the face image features, a new algorithm of face recognition based on wavelet transform and weighted fusion of features is proposed in this study. First, the wavelet transform is used to reduce the dimensionality; then,the features of the four wavelet sub-graphs are extracted by a principal component analysis (PCA), and the features of the four parts are fused into discriminant features. Finally, the features are classified and recognized by SVM. Experimental results on the ORL face database show that the proposed algorithm achieves a recognition accuracy of 97.5 percent, so the new algorithm can effectively improve the face recognition ability. It has a higher recognition accuracy than traditional methods.