双树复小波多频带类内类间不确定度融合的人脸识别  

Face Recognition of Dual-tree Complex Wavelet Multi-frequency Within-class and Inter-class Uncertainty Fusion

作　　者：王仕民[1,2] 叶继华[1] 王明文[1] 程柏良[1,2] 

机构地区：[1]江西师范大学计算机信息工程学院,南昌330022 [2]同济大学嵌入式系统与服务计算教育部重点实验室,上海200092

出　　处：《计算机科学》2014年第2期87-90,共4页Computer Science

基　　金：国家自然科学基金(61262036;61262015);同济大学嵌入式系统与服务计算教育部重点实验室开放基金项目;江西省光电子与通信重点实验项目(2011010);江西省分布计算工程技术研究中心项目(2012006);江西省教育厅科研项目(GJJ13228);江西师范大学青年成长基金资助

摘　　要：为了更好地获取人脸的纹理特征和解决人脸多频带的权值问题,提出了双树复小波多频带类内类间不确定度特征融合的人脸识别算法。首先使用了人脸双树复小波多频带特征构建人脸的纹理特征,引入了双树复小波多频带类内类间的不确定度计算多频带特征权值,同时采用了二维主成份分析方法对人脸多频带特征进行重构线性子空间,人脸子空间加权融合得到的最终特征能够保证投影后样本在新的空间中有最小的类内距离和最大的类间距离。使用ORL人脸图像库进行了实验与分析,结果表明所提出的方法比经典的二维主成份分析、传统小波、Gabor小波和双树复小波方法取得了更好的识别效果。In order to better obtain face texture features for representing face and solve the problem of the face multi- frequency weights, this paper proposed dual-tree complex wavelet multi-frequency within-class and inter-class uncertain- ty fusion in face recognition. Dual-tree complex wavelet multi-frequency features are first used to show face texture fea- tures, Dual-tree complex wavelet multi-frequency within-class and inter-class uncertainties are calculated to get multi- frequency uncertainty weights, at the same time two-dimensional principal component analysis method is exploited to construct the linear subspace for face multi-frequency features, and the final face features from face subspace weighted fusion can ensure that the projected sample has minimum within-class distance and the maximum inter-class in the new space. The experimental results on ORL database and comparative analysis indicate that compared with the classical two-dimensional principal component analysis, traditional wavelet,Gabor wavelet and dual-tree complex wavelet feature extraction method, the proposed method in this paper obtains better recognition rate.

关 键 词：人脸识别 双树复小波 类内类间不确定度 特征融合 二维主成份分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]