|本期目录/Table of Contents|

[1]简彩仁,夏靖波*.基于局部强化最小二乘回归分类法的人脸识别方法[J].厦门大学学报(自然科学版),2019,58(01):122-126.[doi:10.6043/j.issn.0438-0479.201809025]
 JIAN Cairen,XIA Jingbo*.Face recognition using local strengthen least-square regression classification method[J].Journal of Xiamen University(Natural Science),2019,58(01):122-126.[doi:10.6043/j.issn.0438-0479.201809025]
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基于局部强化最小二乘回归分类法的人脸识别方法(PDF/HTML)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
58卷
期数:
2019年01期
页码:
122-126
栏目:
研究论文
出版日期:
2019-01-24

文章信息/Info

Title:
Face recognition using local strengthen least-square regression classification method
文章编号:
0438-0479(2019)01-0122-05
作者:
简彩仁夏靖波*
厦门大学嘉庚学院信息科学与技术学院,福建 漳州 363105
Author(s):
JIAN CairenXIA Jingbo*
School of Information Science & Technology,Xiamen University Tan Kah kee College,Zhangzhou 363105,China
关键词:
人脸识别 最小二乘回归 局部强化 自适应 分类
Keywords:
face recognition least square regression local strengthen adaptive classification
分类号:
TP 311; TP 371
DOI:
10.6043/j.issn.0438-0479.201809025
文献标志码:
A
摘要:
为解决基于表示理论的分类法未考虑噪声样本对重构系数影响的不足,利用局部约束协同表示法改进最小二乘回归分类法,提出局部强化最小二乘回归分类法.该方法通过非负稀疏表示自适应选择近邻样本,并利用近邻样本的协同作用强化重构系数使得局部强化最小二乘回归分类法具有较好的鲁棒性和容噪性.该方法可以克服传统分类方法存在的过拟合问题.在4个人脸图像数据集上的实验结果表明该方法可以提高人脸识别准确率.
Abstract:
In order to improve the weakness of classification method based on the representation theorythat ignore the influence of noise on the reconstruction coefficients,we propose a local strengthen least-square regression classification method to improve the least-square regression classification method by using the local-constraint cooperative representation.The proposed method can select neighbor samples adaptively by using nonnegative sparse representation.It strengthens reconstruction coefficients by using neighbor data samples,and improves anti-noise ability.Furthermore,it can overcome the over-fitting problems that plague traditional classification methods.Experimental results on the four face recognition datasets show that this method can improve recognition accuracies.

参考文献/References:

[1] TOLBA A S,EL-BAZ A H,EL-HARBY A A A.Face recognition:a literature review[J].International Journal of Signal Processing,2008,2(1):88-103.
[2] 吴长虹,苏剑波,陈叶飞.抗年龄干扰的人脸识别[J].电子学报,2018,46(7):1593-1600.
[3] 严严,陈日伟,王菡子.基于深度学习的人脸分析研究进展[J].厦门大学学报(自然科学版),2017,56(1):13-24.
[4] GAO Y,MA J,YUILLE A L.Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples[J].IEEE Transactions on Image Processing,2017,26(5):2545-2560.
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[6] WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[7] 任梓涵,杨双远.基于视觉跟踪的实时视频人脸识别[J].厦门大学学报(自然科学版),2018,57(3):438-444.
[8] 付晓峰,张予,吴俊.遮挡表情变化下的联合辅助字典学习与低秩分解人脸识别[J].中国图象图形学报,2018,23(3):399-409.
[9] 周志华.机器学习[M].北京:清华大学出版社,2016:121-139.
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[11] CHEN X,JIAN C.A tumor classification model using least square regression[C]∥International Conference on Natural Computation.Xiamen:ICNC,2014:753-758.
[12] 简彩仁,陈晓云.基于稀疏表示和最小二乘回归的基因表达数据分类方法[J].福州大学学报(自然科学版),2015,43(6):738-741.
[13] LIN G,YANG M,YANG J,et al.Robust,discriminative and comprehensive dictionary learning for face recognition[J].Pattern Recognition,2018,81(9):341-356.
[14] GOU J,XU Y,ZHANG D,et al.Two-phase linear reconstruction measure-based classification for face recognition[J].Information Sciences,2018,4(433):17-36.
[15] KIM S J,KOH K,LUSTIG M,et al.An interior-point method for large-scale l1-regularized least squares[J].IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617.
[16] HOERL A E,KENNARD R W.Ridge regression:biased estmation for nonorthogoral problems[J].Technometrics,1970,12(1):55-67.[1] TOLBA A S,EL-BAZ A H,EL-HARBY A A A.Face recognition:a literature review[J].International Journal of Signal Processing,2008,2(1):88-103.
[2] 吴长虹,苏剑波,陈叶飞.抗年龄干扰的人脸识别[J].电子学报,2018,46(7):1593-1600.
[3] 严严,陈日伟,王菡子.基于深度学习的人脸分析研究进展[J].厦门大学学报(自然科学版),2017,56(1):13-24.
[4] GAO Y,MA J,YUILLE A L.Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples[J].IEEE Transactions on Image Processing,2017,26(5):2545-2560.
[5] 雷明仪,苏松志,李绍滋.卡口场景下人脸检测模型的自适应重训练算法[J].厦门大学学报(自然科学版),2017,56(3):429-436.
[6] WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[7] 任梓涵,杨双远.基于视觉跟踪的实时视频人脸识别[J].厦门大学学报(自然科学版),2018,57(3):438-444.
[8] 付晓峰,张予,吴俊.遮挡表情变化下的联合辅助字典学习与低秩分解人脸识别[J].中国图象图形学报,2018,23(3):399-409.
[9] 周志华.机器学习[M].北京:清华大学出版社,2016:121-139.
[10] LI Y,NGOM A.Classification approach based on non-negative least squares[J].Neurocomputing,2013,118(11):41-57.
[11] CHEN X,JIAN C.A tumor classification model using least square regression[C]∥International Conference on Natural Computation.Xiamen:ICNC,2014:753-758.
[12] 简彩仁,陈晓云.基于稀疏表示和最小二乘回归的基因表达数据分类方法[J].福州大学学报(自然科学版),2015,43(6):738-741.
[13] LIN G,YANG M,YANG J,et al.Robust,discriminative and comprehensive dictionary learning for face recognition[J].Pattern Recognition,2018,81(9):341-356.
[14] GOU J,XU Y,ZHANG D,et al.Two-phase linear reconstruction measure-based classification for face recognition[J].Information Sciences,2018,4(433):17-36.
[15] KIM S J,KOH K,LUSTIG M,et al.An interior-point method for large-scale l1-regularized least squares[J].IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617.
[16] HOERL A E,KENNARD R W.Ridge regression:biased estmation for nonorthogoral problems[J].Technometrics,1970,12(1):55-67.

备注/Memo

备注/Memo:
收稿日期:2018-09-19 录用日期:2018-10-24
基金项目:福建省自然科学基金(2018J01101)
*通信作者:jbxiad@xujc.com
更新日期/Last Update: 1900-01-01