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基于图像分解的人脸特征表示
引用本文:李照奎,丁立新,何进荣,胡庆辉.基于图像分解的人脸特征表示[J].软件学报,2014,25(9):2102-2118.
作者姓名:李照奎  丁立新  何进荣  胡庆辉
作者单位:软件工程国家重点实验室(武汉大学 计算机学院), 湖北 武汉 430072;沈阳航空航天大学 计算机学院, 辽宁 沈阳 110136;软件工程国家重点实验室(武汉大学 计算机学院), 湖北 武汉 430072;软件工程国家重点实验室(武汉大学 计算机学院), 湖北 武汉 430072;软件工程国家重点实验室(武汉大学 计算机学院), 湖北 武汉 430072
基金项目:国家自然科学基金(60975050, 60902053, 61170185); 广东省省部产学研结合专项资金(2011B090400477); 珠海市产学研合作专项资金(2011A050101005, 2012D0501990016); 珠海市重点实验室科技攻关项目(2012D0501990026)
摘    要:提出一种基于图像分解的人脸特征表示方法(FRID),首先通过多方向操作,把一幅图像分解成一系列方向子图像;然后,通过欧拉映射操作,把每幅方向子图像分解成实部和虚部图像,针对每幅实部和虚部图像,分别划分出多个不重叠的局部图像块,通过统计图像块上不同数值的个数生成相应的实部和虚部直方图,一幅图像的所有实部和虚部直方图被串联成一个超级特征向量;最后,利用线性判别分析方法对超级特征向量进行维数约简,以获得每幅图像的低维表示.实验显示该方法在多个人脸数据库上获得了优于时新算法的识别结果,并且表现得更为稳定.

关 键 词:图像分解  多方向操作  欧拉映射  人脸识别
收稿时间:4/6/2014 12:00:00 AM
修稿时间:2014/5/14 0:00:00

Face Feature Representation Based on Image Decomposition
LI Zhao-Kui,DING Li-Xin,HE Jin-Rong and HU Qing-Hui.Face Feature Representation Based on Image Decomposition[J].Journal of Software,2014,25(9):2102-2118.
Authors:LI Zhao-Kui  DING Li-Xin  HE Jin-Rong and HU Qing-Hui
Affiliation:State Key Laboratory of Software Engineering (School of Computer, Wuhan University), Wuhan 430072, China;School of Computer, Shenyang Aerospace University, Shenyang 110136, China;State Key Laboratory of Software Engineering (School of Computer, Wuhan University), Wuhan 430072, China;State Key Laboratory of Software Engineering (School of Computer, Wuhan University), Wuhan 430072, China;State Key Laboratory of Software Engineering (School of Computer, Wuhan University), Wuhan 430072, China
Abstract:This paper presents a face feature representation method based on image decomposition (FRID). FRID first decomposes an image into a series of orientation sub-images by executing multiple orientations operator. Then, each orientation sub-image is decomposed into a real part image and an imaginary part image by applying Euler mapping operator. For each real and imaginary part image, FRID divides them into multiple non-overlapping local blocks. The real and imaginary part histograms are calculated by accumulating the number of different values of image blocks respectively. All the real and imaginary part histograms of an image are concatenated into a super-vector. Finally, the dimensionality of the super-vector is reduced by linear discriminant analysis to yield a low-dimensional, compact, and discriminative representation. Experimental results show that FRID achieves better results in comparison with state-of-the-art methods, and is the most stable method.
Keywords:image decomposition  multiple orientations operator  Euler mapping  face recognition
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