| 分块MMC及其在人脸识别中的应用 |
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| 引用本文： | 刘辉,万鸣华,王巧丽. 分块MMC及其在人脸识别中的应用[J]. 计算机科学, 2015, 42(8): 78-81 |
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| 作者姓名： | 刘辉 万鸣华 王巧丽 |
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| 作者单位： | 南昌航空大学无损检测技术教育部重点实验室 南昌330063,南昌航空大学无损检测技术教育部重点实验室 南昌330063;南京理工大学高维信息智能感知与系统教育部重点实验室 南京210094,南昌航空大学无损检测技术教育部重点实验室 南昌330063 |
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| 基金项目： | 本文受高维信息智能感知与系统教育部重点实验室(南京理工大学)基金(30920140122006),中国博士后基金(2013M530223),江苏省博士后基金(1301095C),国家自然科学基金(61203243),江西省自然科学基金(20122BAB211025),南昌航空大学研究生创新专项基金(YC2013-013)资助 |
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| 摘 要： | 用最大间距准则(Maximum Margin Criterion,MMC)算法进行特征提取时,提取的是全局的特征,对局部的特征不能有效地抽取。因此,对MMC算法进行改进,提出一种基于分块MMC(Modular Maximum Margin Criterion,MMMC)的人脸识别方法。首先对图像矩阵进行分块,然后对分块后的矩阵进行MMC特征抽取,对每一子块抽取的特征进行整体融合,最后采用最近邻判决准则进行分类识别。在ORL、Yale人脸图像库进行的实验结果表明,新算法相比于MMC算法有更好的识别性能。
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| 关 键 词： | 最大间距准则 分块最大间距准则 人脸识别 特征提取 |
Modular MMC and its Application in Face Recognition |
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| LIU Hui,WAN Ming-hua and WANG Qiao-li. Modular MMC and its Application in Face Recognition[J]. Computer Science, 2015, 42(8): 78-81 |
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| Authors: | LIU Hui WAN Ming-hua WANG Qiao-li |
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| Affiliation: | Key Laboratory of Nondestructive Testing,Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China,Key Laboratory of Nondestructive Testing,Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China;Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education,Nanjing University of Science and Technology,Nanjing 210094,China and Key Laboratory of Nondestructive Testing,Ministry of Education,Nanchang Hangkong University,Nanchang 330063,China |
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| Abstract: | Maximum margin criterion(MMC) algorithm for feature extraction only extracts global features while local features can not be effectively extracted.So,an improved version of maximum margin criterion(MMC) named modular maximum margin criterion(MMMC) was proposed in this paper.First,in proposed approach,the original images are divided into modular images,which are also called sub-images.Then,MMC method is directly used to extract the features of the sub-images from the previous step.Features of sub-images are combined into global features.At last,the recognition results are obtained by nearest neighbor(NN) classifier.The results of test on ORL,Yale and AR face database show that the proposed algorithm with respect to the MMC algorithm has better recognition performance. |
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| Keywords: | Maximum margin criterion Modular maximum margin criterion Face recognition Feature extraction |
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