Hand shape recognition based on fusion features of fingers and particle swarm optimization

LIU Fu; LIU Hui-ying; GAO Lei; LI Tian-yu

doi:10.3788/OPE.20152306.1774

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Hand shape recognition based on fusion features of fingers and particle swarm optimization

更新时间：2020-08-13

- Hand shape recognition based on fusion features of fingers and particle swarm optimization
- Optics and Precision Engineering Vol. 23, Issue 6, Pages: 1774-1782(2015)
- 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：
- DOI：10.3788/OPE.20152306.1774
  CLC： TP391.4
- Received：15 January 2015，
  
  Revised：19 March 2015，
  
  Published：25 June 2015
- 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

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Related Institution

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