在人脸识别中,传统小波、Gabor小波不能很好地表征人脸特征。提出2维双树复小波多频带不确定度加权融合的人脸识别算法,使用了人脸2维双树复小波多频带特征,计算多频带不确定度及其权值并结合多频带特征进行加权融合,能很好得到人脸的特征。该加权融合算法首先计算人脸2维双树复小波多个频带特征图,然后计算多个频带滤波不确定度权值,最后进行加权融合。同时使用了2维主成分分析(2DPCA)方法对特征向量进行子空间投影,应用欧氏距离作为相似测度实现分类识别。使用英国剑桥Olivetti实验室(ORL)图像库进行了测试,实验结果表明,提出的方法相对于使用2DPCA、Wavelet和Gabor小波的特征提取方法,取得了更好的识别效果。

The traditional wavelet and Gabor wavelet cannot show facial features well in face recognition. In this paper, we propose a 2D dual-tree complex wavelet multi-frequency uncertainty weighted fusion for face recognition. 2D dual-tree complex wavelet multi-frequency features are used to show facial features. Weights and uncertainties are calculated to get the last facial feature by multi-frequency uncertainty weighted fusion algorithm. The weighted fusion algorithm first calculates the 2D DT-CWT multi-frequency filter images of the face, and then the uncertainty weights of the multi-frequency filters are calculated. Finally, the 2D DT-CWT multi-frequency filter is integrated into the last facial feature. At the same time, the 2D-PCA method is exploited to construct the linear subspace. The Euclidean distance based classifier is adopted for classification. Using the ORL database, the experimental results indicated that compared with the use of 2D-PCA, Wavelet, and Gabor wavelet feature extraction methods, the proposed method obtains a better recognition rate.