首页 | 本学科首页   官方微博 | 高级检索  
     

基于双密度双树复小波变换多字典的人脸特征稀疏分类方法
引用本文:王成语,李伟红.基于双密度双树复小波变换多字典的人脸特征稀疏分类方法[J].计算机应用,2011,31(8):2115-2118.
作者姓名:王成语  李伟红
作者单位:重庆大学 光电技术及系统教育部重点实验室,重庆400030
基金项目:重庆市科技攻关重点项目,中央高校基本科研业务费专项,重庆市科委自然科学基金资助项目
摘    要:基于超完备字典的人脸稀疏表示方法的难点是其字典构成。针对此问题,首先采用双密度双树复小波变换(DD-DT CWT)提取人脸图像不同尺度的高频子带,然后根据能量平均分布最大原则选择能量较大的部分子带构成对应尺度的超完备字典。同时,将测试样本相应的人脸DD-DT CWT子带特征看成超完备字典中原子的线性组合,并组合多字典上的稀疏表示进行识别。在AR人脸图像库上进行了实验,结果表明该方法是一种有效的人脸特征表示及分类方法。

关 键 词:超完备字典    稀疏表示    双密度双树复小波    特征提取    多尺度
收稿时间:2011-03-03
修稿时间:2011-04-25

Sparse representation of face feature recognition based on multiple dictionaries of double-density dual-tree complex wavelet transform
WANG Cheng-yu,LI Wei-hong.Sparse representation of face feature recognition based on multiple dictionaries of double-density dual-tree complex wavelet transform[J].journal of Computer Applications,2011,31(8):2115-2118.
Authors:WANG Cheng-yu  LI Wei-hong
Affiliation:Key Laboratory for Opto-electronic Technology and System of Ministry of Education, Chongqing University, Chongqing 400030, China
Abstract:The difficulty in sparse representation of facial images based on over-complete dictionary is the dictionary generation. This paper first introduced the Double-Density Dual-Tree Complex Wavelet Transform (DD-DT CWT) for filtering the high-frequency sub-bands and the principle of energy distribution for selecting some sub-bands as the feature of a facial image to form multi-scale dictionaries, then viewed the similar feature of a test sample as the linear combination of some atoms in the overcomplete dictionary, finally got the recognition results via ensembling sparse representations on these dictionaries. The experimental results on AR face database demonstrate the efficiency of the proposed algorithm.
Keywords:
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《计算机应用》浏览原始摘要信息
点击此处可从《计算机应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号