作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程

• 图形图像处理 • 上一篇    下一篇

基于概率提升树的虹膜分割算法

周恺,苏娟   

  1. (湖南大学 电气与信息工程学院,长沙 410082)
  • 收稿日期:2016-05-31 出版日期:2017-08-15 发布日期:2017-08-15
  • 作者简介:周恺(1990—),男,硕士研究生,主研方向为图像处理、机器视觉、模式识别;苏娟,副教授。
  • 基金资助:
    湖南省科技计划项目(2014GK3007)。

Iris Segmentation Algorithm Based on Probabilistic Boosting Tree

ZHOU Kai,SU Juan   

  1. (College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)
  • Received:2016-05-31 Online:2017-08-15 Published:2017-08-15

摘要: 针对传统边缘检测算法无法自动提取虹膜边缘的问题,提出一种基于监督学习的边缘检测虹膜分割算法。提取边界点样本的多尺度Haar和Hog特征,以概率提升树作为训练框架训练出瞳孔、虹膜、眼皮的概率提升树分类模型,将测试样本输入概率提升树后输出边界点为真实虹膜边界的概率,并对分类输出的真实虹膜轮廓边界进行拟合,最终利用局部OTSU算法实现虹膜的精确分割。实验结果表明,与基于霍夫变换和活动轮廓模型的虹膜分割算法相比,该算法具有更少的测试时间和更低的分割错误率。

关键词: 虹膜分割, 概率提升树, 边缘检测, 监督学习, 轮廓提取

Abstract: Aiming at the problem that the traditional edge detection algorithm cannot automatically extract iris contour from the edge points,a edge detection algorithm based on supervised learning for iris segmentation is proposed in this paper.A set of features including Haar and Hog in multi-scale is used to characterize the edge points.The probabilistic boosting tree is used as a training framework to train the pupil,iris and eyelid as a probabilistic boosting tree model.The test samples are input to calculate the probability of the truth iris contour.The output true iris contour edges are fitted,and the local OTSU algorithm is used to segment the iris accurately.Experimental results show that,compared with iris segmentation algorithm based on Hough transform and active contour model,this algorithm has less test time and lower error rate.

Key words: iris segmentation, Probabilistic Boosting Tree(PBT), edge detection, supervised learning, contour extraction

中图分类号: