[1]王科俊,邹国锋,张洁.SPCA参数对单样本人脸识别效果影响分析[J].智能系统学报,2011,6(6):531-538.
WANG Kejun,ZOU Guofeng,ZHANG Jie.Analysis of the influence of SPCA parameters on the recognition of a single sample face[J].CAAI Transactions on Intelligent Systems,2011,6(6):531-538.
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WANG Kejun,ZOU Guofeng,ZHANG Jie.Analysis of the influence of SPCA parameters on the recognition of a single sample face[J].CAAI Transactions on Intelligent Systems,2011,6(6):531-538.
SPCA参数对单样本人脸识别效果影响分析
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
6
期数:
2011年第6期
页码:
531-538
栏目:
学术论文—机器感知与模式识别
出版日期:
2011-12-25
- Title:
- Analysis of the influence of SPCA parameters on the recognition of a single sample face
- 文章编号:
- 1673-4785(2011)06-0531-08
- Keywords:
- face recognition; singular value decomposition; (PC)2A; SPCA; derived image; combined image
- 分类号:
- TP391.4
- 文献标志码:
- A
- 摘要:
- 奇异值扰动的主分量分析（SPCA）是一种有效的单样本人脸识别方法，但SPCA算法的识别效果受参数选择的影响比较大，针对SPCA算法中衍生图像生成参数n和结合参数α的不同取值对识别效果的影响进行了分析，利用ORL人脸库和CASPEAL人脸库做了大量的实验和比较分析，实验结果表明给出的SPCA参数选取方法和取值范围是合理的，并有效地提高了SPCA算法的实际应用效果和单样本人脸识别的性能.
- Abstract:
- Singular value decomposition perturbation principal component analysis (SPCA) is an effective singlesample face recognition method; however, the identification results of the SPCA algorithm are seriously affected by parameter selection. In this paper, the effect on the identification, which was caused by the derived image parameter and the combined image generation parameter in the SPCA algorithm, was analyzed. Many experiments and comparative analyses were performed on the basis of the ORL face database and the CASPEAL face database. The experimental results show that the SPCA parameter selection method and the parameter range given in this paper are reasonable. In addition, reasonable parameters are effective in improving practical application of SPCA algorithms and the recognition performance of a singlesample face.
- 参考文献/References:
-
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WANG Kejun, DUAN Shengli, FENG Weixing. A survey of face recognition using single training sample［J］. Pattern Recognition and Artificial Intelligence, 2008, 21(5): 7783， 635642.
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［6］WU Jianxin, ZHOU Zhihua. Face recognition with one training image per person［J］. Pattern Recognition Letters, 2002, 23(14): 17111719.
［7］CHEN Songcan, ZHANG Daoxiang，ZHOU Zhihua. Enhanced (PC)2A for face recognition with one training image per person［J］. Pattern Recognition Letters， 2004， 25(10)： 11731181.
［8］何家忠，杜明辉.单样本人脸识别中一种新的图像增强方法［J］.微计算机信息， 2006, 22(3): 266268.
HE Jiazhong,DU Minghui. A new image enhancement method for face recognition with single training sample［J］.Microcomputer Information, 2006, 22(3): 266268.
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备注/Memo
收稿日期： 2010-09-14.
基金项目：国家“863”计划资助项目(2008AA01Z148).?
通信作者：邹国锋.E-mail:zgf841122@163.com.
作者简介：
王科俊，男，1962年生，教授，博士生导师，博士，哈尔滨工程大学自动化学院副院长，哈尔滨工程大学模式识别与智能系统学科带头人.现任中国人工智能学会理事、中国人工智能学会科普工作委员会副主任、黑龙江省人工智能学会理事长、黑龙江省神经科学学会副理事长、黑龙江省神经科学学会人工智能与医学工程专业委员会主任、黑龙江省自动化学会理事.曾获得部级科技进步二等奖2项，三等奖3项，省高校科学技术一等奖1项、二等奖1项.主要研究方向为生物特征识别与智能监控、神经网络、计算生物信息学等.完成科研项目20余项，在研项目10余项.发表学术论文150余篇，出版学术专著3部，主审教材2部.
邹国锋，男，1984年生，博士研究生，主要研究方向为生物特征识别与智能监控.
张洁，女，1987年生，硕士研究生，主要研究方向为生物特征识别与智能监控.
更新日期/Last Update:
2012-02-29