基于手指融合特征和粒子群优化的手形识别

刘富; 刘惠影; 高雷; 李天宇

doi:10.3788/OPE.20152306.1774

您当前的位置：

首页 >

文章列表页 >

基于手指融合特征和粒子群优化的手形识别

信息科学 | 更新时间：2020-08-13

- 基于手指融合特征和粒子群优化的手形识别
- Hand shape recognition based on fusion features of fingers and particle swarm optimization
- 光学精密工程 2015年23卷第6期页码：1774-1782
- 作者机构：
  
  1. 吉林大学通信工程学院,吉林长春,中国,130012
  2. 吉林大学机械科学与工程学院,吉林长春,中国,130025
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51105170)();吉林省科技发展计划资助项目(No.10100505)()
- DOI：10.3788/OPE.20152306.1774
  中图分类号： TP391.4
- 收稿日期：2015-01-15，
  
  修回日期：2015-03-19，
  
  纸质出版日期：2015-06-25
- 稿件说明：
移动端阅览
刘富, 刘惠影, 高雷等. 基于手指融合特征和粒子群优化的手形识别[J]. 光学精密工程, 2015,23(6): 1774-1782

LIU Fu, LIU Hui-ying, GAO Lei etc. Hand shape recognition based on fusion features of fingers and particle swarm optimization[J]. Editorial Office of Optics and Precision Engineering, 2015,23(6): 1774-1782
刘富, 刘惠影, 高雷等. 基于手指融合特征和粒子群优化的手形识别[J]. 光学精密工程, 2015,23(6): 1774-1782 DOI： 10.3788/OPE.20152306.1774.

LIU Fu, LIU Hui-ying, GAO Lei etc. Hand shape recognition based on fusion features of fingers and particle swarm optimization[J]. Editorial Office of Optics and Precision Engineering, 2015,23(6): 1774-1782 DOI： 10.3788/OPE.20152306.1774.

摘要

针对基于手部几何特征的手形识别方法可利用的个体信息有限的问题

提出了一种将手指轮廓特征与几何特征相融合的身份识别方法.该算法首先分离手指

采用曲线拟合算法定位手指中轴线;然后采用分步对齐方法规范化手指

并提取手指轮廓特征和几何特征;最后采用粒子群算法对手指截取系数和权值系数进行优化

以进一步提高识别准确率.实验结果表明:采用该方法后

识别率可达98.61%.该方法手指定位更准确

更充分地利用了手部信息

且避免了特征点定位不准及手指根部不稳定轮廓特征对识别准确率的影响

具有较高的识别率和良好的鲁棒性.

Abstract

As the hand shape recognition method based on geometric features is limited by its individual information

this paper presents a new hand shape recognition method based on the fusion of finger contour features and geometric features. A curve fitting method was proposed to position the axis of finger after four fingers were separated. Then the matched fingers were normalized by stepwise alignment method

and the contour features and geometric features were extracted. Finally

the Particle Swarm Optimization (PSO) was used to optimize the cut-off coefficients and the weight values of fingers to further improve the recognition rate. Experimental results show that the recognition rate by proposed method is 98.61%. As the four fingers are separated

the proposed method reduces the computing consumption and gets more accurate located hand shapes. With make full use of the hand information

it also avoids the influence of inaccurate feature point location and instable contour around finger valleys on the recognition accuracy

and has high recognition rate and good robustness.

关键词

Keywords

references

JAIN A K, ROSS A, PRABHAKAR S. An introduction to biometric recognition [J].IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1): 4-20.

GUO J M, HSIA C H, LIU Y F,et al.. Contact-free hand geometry-based identification system [J]. Expert Systems with Applications, 2012, 39(14): 11728-11736.

FERRER M A, MORALES A, ALONSO J B. Fingers shape biometric identification using point distribution models [J]. Electronics Letters, 2010, 46(7): 495-497.

KUMAR A, WONG D C M, SHEN H C,et al.. Personal verification using palmprint and hand geometry biometric [J]. Lecture Notes in Computer Science, 2003: 668-678.

SAVIČ T, PAVEŠI Ć N. Personal recognition based on an image of the palmar surface of the hand [J].Pattern Recognition, 2007, 40(11): 3152-3163.

MICHAEL G K O, CONNIE T, TEOH A B J. A contactless biometric system using multiple hand features [J]. Journal of Visual Communication and Image Representation, 2012, 23(7): 1068-1084.

苑玮琦, 董茜. 基于手部尺寸特征的手形认证方法[J]. 光学学报, 2010,30 (10): 2994-2999. YUAN W Q, DONG Q. Hand shape verification method based on hand geometry feature [J]. Acta Optica Sinica, 2010, 30 (10): 2994-2999. (in Chinese)

林森, 苑玮琦, 荆澜涛, 等. LST 变换和手部几何特征融合的模糊掌纹识别[J]. 仪器仪表学报, 2013, 34(2): 415-422. LIN S, YUAN W Q, JING L T, et al.. Blurred palm-print recognition based on fusion of Laplacian smoothing transform and geometric features of hand [J]. Chinese Journal of Scientific Instrument, 2013, 34(2): 415-422. (in Chinese)

KANHANGAD V, KUMAR A, ZHANG D. A unified framework for contactless hand verification [J].IEEE Transactions on Information Forensics and Security, 2011, 6(3): 1014-1027.

KANHANGAD V, KUMAR A, ZHANG D. Contactless and pose invariant biometric identification using hand surface [J].IEEE Transactions on Image Processing, 2011, 20(5): 1415-1424.

MICHAEL G K O, CONNIE T, TEOH A B J. A contactless biometric system using multiple hand features [J]. Journal of Visual Communication and Image Representation, 2012, 23(7): 1068-1084.

ASAARI M S M, SUANDI S A, ROSDI B A. Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics [J]. Expert Systems with Applications, 2014, 41(7): 3367-3382.

何志勇, 孙立宁, 黄伟国, 等. 基于Otsu准则和直线截距直方图的阈值分割[J]. 光学精密工程, 2012, 20(10): 2315-2323. HE ZH Y, SUN L N, HUANG W G,et al.. Thresholding segmentation algorithm based on Otsu criterion and line intercept histogram [J]. Opt. Precision Eng., 2012, 20(10): 2315-2323. (in Chinese)

苑玮琦, 朱春艳, 柯丽. 手指宽度选取与识别率对应关系分析[J]. 光学精密工程, 2009, 17(7): 1730-1736. YUAN W Q, ZHU CH Y, KE L. Analysis of relationship between finger width and recognition rate [J]. Opt. Precision Eng., 2009, 17(7): 1730-1736. (in Chinese)

ADÁN M, ADÁN A, VÁZQUEZ A S, et al.. Biometric verification/identification based on hands natural layout [J]. Image and Vision Computing, 2008, 26(4): 451-465.

SHARMA S, DUBEY S R, SINGH S K, et al.. Identity verification using shape and geometry of human hands [J]. Expert Systems with Applications, 2015, 42(2): 821-832.

储珺, 郭卢, 安政, 等. 采用环形模板的棋盘格角点检测[J]. 光学精密工程, 2013, 21(1): 189-196. CHU J, GUO L, AN ZH, et al.. Chessboard corner detection based on circular template [J] Opt. Precision Eng., 2013, 21(1): 189-196. (in Chinese)

ALFI A. Particle swarm optimization algorithm with dynamic inertia weight for online parameter identification applied to Lorenz chaotic system [J].International Journal of Innovative Computing Information and Control, 2012, 8(2): 1191-1203.

邹德旋, 王鑫, 陈传虎, 等. 基于改进粒子群的虹膜定位算法[J]. 光学精密工程, 2014, 22(4): 1056-1063. ZOU D X, WANG X, CHEN CH H, et al.. Iris location algorithm based on improved particle swarm optimization [J]. Opt. Precision Eng., 2014, 22(4): 1056-1063. (in Chinese)

浏览量

420

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于分数阶微分的高光谱图像特征提取与分类

面向高光谱影像场景分类的轻量化深度全局-局部知识蒸馏网络

多分支无锚框网络密集行人检测算法

SENet生成对抗网络在图像语义描述中的应用

面向高光谱图像分类的半监督双流网络