基于极限学习机与子空间追踪的人脸识别算法
极限学习机（ELM）与稀疏表示分类（SRC）算法被广泛应用于人脸识别中。ELM学习速度快,但不能很好地处理噪声图像,SRC对噪声具有鲁棒性,但计算复杂度较高。针对上述2种算法的优缺点,利用子空间追踪算法求解稀疏系数,提出一种改进的人脸识别算法,从而达到高识别率与快速的识别效果。该算法根据测试样本的ELM实际输出向量判断是否为噪声图像,干净图像直接依据ELM输出向量进行分类,噪声图像采用子空间追踪算法结合SRC框架来分类。在扩展的Yale B和ORL人脸数据库上的实验结果表明,该算法不仅识别率高,且识别速度快。...
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| Published in | 计算机工程 Vol. 42; no. 1; pp. 168 - 173 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
长沙理工大学计算机与通信工程学院,长沙,410114
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-3428 |
| DOI | 10.3969/j.issn.1000-3428.2016.01.030 |
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| Abstract | 极限学习机（ELM）与稀疏表示分类（SRC）算法被广泛应用于人脸识别中。ELM学习速度快,但不能很好地处理噪声图像,SRC对噪声具有鲁棒性,但计算复杂度较高。针对上述2种算法的优缺点,利用子空间追踪算法求解稀疏系数,提出一种改进的人脸识别算法,从而达到高识别率与快速的识别效果。该算法根据测试样本的ELM实际输出向量判断是否为噪声图像,干净图像直接依据ELM输出向量进行分类,噪声图像采用子空间追踪算法结合SRC框架来分类。在扩展的Yale B和ORL人脸数据库上的实验结果表明,该算法不仅识别率高,且识别速度快。 |
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| AbstractList | TP18; 极限学习机(ELM)与稀疏表示分类(SRC)算法被广泛应用于人脸识别中.ELM学习速度快,但不能很好地处理噪声图像,SRC对噪声具有鲁棒性,但计算复杂度较高.针对上述2种算法的优缺点,利用子空间追踪算法求解稀疏系数,提出一种改进的人脸识别算法,从而达到高识别率与快速的识别效果.该算法根据测试样本的ELM实际输出向量判断是否为噪声图像,干净图像直接依据ELM输出向量进行分类,噪声图像采用子空间追踪算法结合SRC框架来分类.在扩展的Yale B和ORL人脸数据库上的实验结果表明,该算法不仅识别率高,且识别速度快. 极限学习机（ELM）与稀疏表示分类（SRC）算法被广泛应用于人脸识别中。ELM学习速度快,但不能很好地处理噪声图像,SRC对噪声具有鲁棒性,但计算复杂度较高。针对上述2种算法的优缺点,利用子空间追踪算法求解稀疏系数,提出一种改进的人脸识别算法,从而达到高识别率与快速的识别效果。该算法根据测试样本的ELM实际输出向量判断是否为噪声图像,干净图像直接依据ELM输出向量进行分类,噪声图像采用子空间追踪算法结合SRC框架来分类。在扩展的Yale B和ORL人脸数据库上的实验结果表明,该算法不仅识别率高,且识别速度快。 |
| Author | 张建明 刘阳春 吴宏林 |
| AuthorAffiliation | 长沙理工大学计算机与通信工程学院,长沙410114 |
| AuthorAffiliation_xml | – name: 长沙理工大学计算机与通信工程学院,长沙,410114 |
| Author_FL | ZHANG Jianming WU Honglin LIU Yangchun |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Face Recognition Algorithm Based on Extreme Learning Machine and Subspace Pursuit |
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| EndPage | 173 |
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| Keywords | 极限学习机 稀疏编码 子空间追踪 face recognition Subspace Pursuit (SP) 人脸识别 sparse coding 稀疏表示 Extreme Learning Machine (ELM) sparse representation |
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| Notes | 31-1289/TP ZHANG Jianming,LIU Yangchun,WU Honglin （ School of Computer ＆ Communication Engineering, Changsha University of Science ＆ Technology, Changsha 410114, China） face recognition; Extreme Learning Machine（ELM）; sparse representation; sparse coding; Subspace Pursuit（SP） Extreme Learning Machine（ELM） and Sparse Representation based Classification（SRC） algorithm are applied to face recognition widely.ELM has speed advantage while it can not handle noise well /whereas SRC shows significant robustness to noise while it suffers high computational cost.According to the advantages and disadvantages of two algorithms,this paper proposes a hybrid approach combining extreme learning machine and Subspace Pursuit（SP） for face recognition,which incorporates their respective advantages and uses subspace pursuit method to optimize solving sparse representation coefficients in SRC.According to the analysis of ELM actual output to estimate whether the test sample is a noisy image,clean image directly uses ELM actual output |
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| PublicationTitle | 计算机工程 |
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| PublicationYear | 2016 |
| Publisher | 长沙理工大学计算机与通信工程学院,长沙,410114 |
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| Snippet | 极限学习机（ELM）与稀疏表示分类（SRC）算法被广泛应用于人脸识别中。ELM学习速度快,但不能很好地处理噪声图像,SRC对噪声具有鲁棒性,但计算复杂度较高。针对上述2种算法的... TP18; 极限学习机(ELM)与稀疏表示分类(SRC)算法被广泛应用于人脸识别中.ELM学习速度快,但不能很好地处理噪声图像,SRC对噪声具有鲁棒性,但计算复杂度较高.针对上述2种算法... |
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| SubjectTerms | 人脸识别 子空间追踪 极限学习机 稀疏编码 稀疏表示 |
| Title | 基于极限学习机与子空间追踪的人脸识别算法 |
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