<-上一篇/Previous Article 下一篇/Next Article->

[1]贲晛烨,王科俊,刘海洋.核方法的对比研究及在步态识别中的应用[J].智能系统学报,2011,6(1):63-67.
　BEN Xianye,WANG Kejun,LIU Haiyang.A comparative study on kernel methods and their applications to gait recognition[J].CAAI Transactions on Intelligent Systems,2011,6(1):63-67.

点击复制

核方法的对比研究及在步态识别中的应用

PDF下载 HTML

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 6 期数: 2011年第1期页码: 63-67 栏目: 学术论文—机器感知与模式识别出版日期: 2011-02-25

Title:: A comparative study on kernel methods and their applications to gait recognition

文章编号:: 1673-4785(2011)01-0063-05

作者:: 贲晛烨¹，王科俊^2，刘海洋^1; 1.哈尔滨工业大学交通科学与工程学院,黑龙江哈尔滨 150090；
2.哈尔滨工程大学自动化学院，黑龙江哈尔滨 150001

Author(s):: BEN Xianye¹, WANG Kejun², LIU Haiyang¹; 1.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China；
?2.College of Automation, Harbin Engineering University, Harbin 150001, China

关键词:: 步态识别; 核主成分分析; 核线性判别分析; 核二维主成分分析; 核二维线性判别分析

Keywords:: gait recognition; kernel principal component analysis (KPCA); kernel linear discriminant analysis (KLDA); kernel two dimensional principal component analysis (K2DPCA); kernel two dimensional linear discriminant analysis (K2DLDA)

分类号:: TP391.41

文献标志码:: A

摘要:: 为了提高步态识别问题的识别性能，将“核技巧”应用到步态识别上，对核二维线性判别分析提出新的解决方案，在自建的HEU(B)步态数据库上，应用核主成分分析、核线性判别分析、核二维主成分分析与核二维线性判别分析进行特征提取作对比实验研究.实验结果显示：“核技巧”用于矩阵特征比向量更有效；核二维主成分分析对于单训练样本较核主成分分析更为有效；核二维线性判别分析在测试识别时间上有优势.

Abstract:: A kernel trick was applied to gait recognition in order to improve recognition performance. A novel solution was proposed for kernel two dimensional linear discriminant analysis. Feature extraction, which makes use of kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA), kernel two dimensional principal component analysis (K2DPCA), and kernel two dimensional linear discriminant analysis (K2DLDA), was performed for contrasting experiments in HEU(B)’s locally built gait database. The experimental results demonstrate that a kernel trick applied to a matrix form is more efficient than in vector form. K2DPCA outperforms KPCA significantly with a single sample per person, and K2DLDA has the advantage of less time spent on recognition testing.

参考文献/References:: ［1］王科俊，侯本博.步态识别综述［J］.中国图象图形学报, 2007, 12(7): 11521160.
WANG Kejun， HOU Benbo. A survey of gait recognition［J］. Journal of Image and Graphics, 2007, 12(7): 11521160.
［2］ WAGG D K, NIXON M S. On automated modelbased extraction and analysis of gait［C］//Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition. Seoul, Korea, 2004: 1116.
［3］RONG Z, CHRISTIAN V, DIMITRIS M. Human gait recognition at sagittal plane［J］. Image and Vision Computing, 2007, 25(3): 321330.
［4］LEE S, LIU Y, COLLINS R. Shape variationbased frieze pattern for robust gait recognition［C］//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 18.
［5］LAM T H W, LEE R S T, ZHANG D. Human gait recognition by the fusion of motion and static spatiotemporal templates［J］. Pattern Recognition, 2007, 40(9): 25632573.
［6］LU Haiping, PLATANIOTIS K N, VENETSANOPOULOS A N. MPCA: multilinear principal component analysis of tensor objects［J］. Neural Networks, 2008, 19(1): 1839.
［7］HUANG P S. Automatic gait recognition via statistical approaches for extended template features［J］. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 2001, 31(5): 818823.
［8］WU Jianning, WANG Jue, LIU Li. Feature extraction via KPCA for classification of gait patterns［J］. Human Movement Science, 2007, 26(3): 393411.
［9］NI Jian, LIANG Libo. A new method based on KFDA and SVM for gait identification［C］//International Workshop on Intelligent Systems and Applications(ISA 2009). Wuhan, China, 2009: 13.
［10］王科俊，贲晛烨.基于广义主成分分析的步态识别算法研究［J］.哈尔滨工程大学学报, 2009, 30(9): 2834.
?WANG Kejun, BEN Xianye. Research on gait recognition based on generalized principal component analysis［J］. Journal of Harbin Engineering University, 2009, 30(9): 2834.
［11］王科俊，贲晛烨，刘丽丽，等.基于能量的信息融合步态识别［J］.华中科技大学学报：自然科学版, 2009, 37(5): 1417.
WANG Kejun, BEN Xianye, LIU Lili, et al. Gait recognition using information fusion of energy［J］. Journal of Huazhong University of Science and Technology: Nature Science Edition, 2009, 37(5): 1417.
［12］LIANG Zhizheng, LI Youfu, SHI Pengfei. A note on twodimensional linear discriminant analysis［J］. Pattern Recognition Letters, 2008, 29(16): 21222128.
［13］ZHANG Daoqiang, CHEN Songcan, ZHOU Zhihua. Recognizing face or object from a single image: linear vs. kernel methods on 2D patterns［C］//Proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition. Hong Kong, China, 2006: 889897.
［14］王科俊，贲晛烨，刘丽丽.基于FanBeam映射的步态识别算法［J］.哈尔滨工业大学学报, 2008, 40（增刊1）: 151155.
WANG Kejun, BEN Xianye, LIU Lili. Gait recognition based on fanbeam projection［J］. Journal of Harbin Institute of Technology, 2008, 40(Supl.1): 151155.

相似文献/References:: [1]张元元,吴晓娟,李秀媛,等.平行线约束下的视角无关步态识别算法[J].智能系统学报,2009,4(3):264.
　ZHANG Yuan-yuan,WU Xiao-juan,LI Xiu-yuan,et al.Viewpointindependent gait recognition with parallel line constraints[J].CAAI Transactions on Intelligent Systems,2009,4():264.
[2]王科俊,贲晛烨,刘丽丽.采用Radon变换和二维主成分分析的步态识别算法[J].智能系统学报,2010,5(3):266.
　WANG Ke-jun,BEN Xian-ye,LIU Li-li.Gait recognition with Radon transform and D principal component analysis[J].CAAI Transactions on Intelligent Systems,2010,5():266.
[3]高海燕,阮秋琦.正面视角的步态识别[J].智能系统学报,2011,6(2):119.
　GAO Haiyan,RUAN Qiuqi.A gait recognition method based on frontview[J].CAAI Transactions on Intelligent Systems,2011,6():119.
[4]杨静,阮秋琦,李小利.基于频谱分析的Procrustes统计步态识别算法[J].智能系统学报,2011,6(5):432.
　YANG Jing,RUAN Qiuqi,LI Xiaoli,et al.A Procrustes statistical gait recognition algorithm based on spectrum analysis[J].CAAI Transactions on Intelligent Systems,2011,6():432.
[5]贲晛烨,王科俊,马慧.视频下的正面人体身份自动识别[J].智能系统学报,2012,7(1):69.
　BEN Xianye,WANG Kejun,MA Hui.Videobased automatic frontview human identification[J].CAAI Transactions on Intelligent Systems,2012,7():69.
[6]李一波,李昆.双视角下多特征信息融合的步态识别[J].智能系统学报,2013,8(1):74.[doi:10.3969/j.issn.1673-4785.201209033]
　LI Yibo,LI Kun.Gait recognition based on dual view and multiple feature information fusion[J].CAAI Transactions on Intelligent Systems,2013,8():74.[doi:10.3969/j.issn.1673-4785.201209033]
[7]贲晛烨,张鹏,潘婷婷,等.线性插值框架下矩阵步态识别的性能分析[J].智能系统学报,2013,8(5):415.[doi:10.3969/j.issn.1673-4785.201110007]
　BEN Xianye,ZHANG Peng,PAN Tingting,et al.Performance analysis of matrix gait recognition under? linear interpolation framework[J].CAAI Transactions on Intelligent Systems,2013,8():415.[doi:10.3969/j.issn.1673-4785.201110007]
[8]李浩淼,张含笑,邢向磊.联合局部多尺度和全局上下文特征的步态识别[J].智能系统学报,2024,19(4):853.[doi:10.11992/tis.202304004]
　LI Haomiao,ZHANG Hanxiao,XING Xianglei.Gait recognition with united local multiscale and global context features[J].CAAI Transactions on Intelligent Systems,2024,19():853.[doi:10.11992/tis.202304004]

备注/Memo

收稿日期：2010-09-09.
基金项目：国家“863”计划资助项目（2008AA01Z148）.
通信作者：贲晛烨.
E-mail:benxianyeye@163.com.
作者简介：
贲晛烨，女，1983年生，博士后.主要研究方向为智能交通系统、模式识别、生物特征识别.申请国家发明专利5项，其中1项已授权.发表学术论文26篇，其中被SCI、EI检索14篇.
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，模式识别与智能系统学科带头人.主要研究方向为模糊混沌神经网络、自适应逆控制理论、可拓控制、网络智能控制、模式识别、多模态生物特征识别、联脱机指纹考试身份鉴别系统、微小型机器人系统等.完成科研项目20余项，目前在研项目10余项.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.发表学术论文180余篇，出版学术专著3部、国防教材1部，主审教材2部.
刘海洋，男，1986年生，硕士研究生，主要研究方向为智能交通系统.

更新日期/Last Update: 2011-04-13

核方法的对比研究及在步态识别中的应用 PDF下载HTML

备注/Memo

核方法的对比研究及在步态识别中的应用

PDF下载 HTML