An algorithm applied in frontal-view face images for automatically localizing eyes

LI Fen-lan; XU Ke-xin

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An algorithm applied in frontal-view face images for automatically localizing eyes

更新时间：2020-08-12

- An algorithm applied in frontal-view face images for automatically localizing eyes
- Optics and Precision Engineering Vol. 14, Issue 2, Pages: 320-326(2006)
- 作者机构：
  
  天津大学精密测试技术及仪器国家重点实验室天津,中国,300072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Received：22 November 2005，
  
  Revised：18 January 2006，
  
  Published Online：30 April 2006，
  
  Published：30 April 2006
- 稿件说明：
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LI Fen-lan, XU Ke-xin. An algorithm applied in frontal-view face images for automatically localizing eyes[J]. Optics and precision engineering, 2006, 14(2): 320-326.
DOI：

LI Fen-lan, XU Ke-xin. An algorithm applied in frontal-view face images for automatically localizing eyes[J]. Optics and precision engineering, 2006, 14(2): 320-326. DOI：

摘要

基于人眼轮廓所具有的椭圆特性

提出了一种眼睛定位算法。首先对人脸图像边缘提取

并对边缘所有点进行最小平方椭圆拟合

根据眼睛宽度与高度比例

给定一个阈值

确定眼睛候选区域;然后运用训练过的支持向量机(SVM)分类器验证眼睛区域;最后在眼睛区域

采用一种基于方向性圆形霍夫变换(Circle Hough Transform (CHT))的检测算子定位眼球中心

并对ORL及自建人脸库进行了测试。结果表明:分别在1.25 s

3.0 s内

验证率为91.6%

95%的情况下

该方法可以获得7.26和3.45个像素的平均定位误差

同时也证实了该方法对人脸的平面内旋转、尺度、表情等变化具有较强的鲁棒性。

Abstract

Based on the ellipse-like outline of human eyes

an algorithm for localizing eyes was presented. After edge extracting from the face image

least square ellipse fitting was performed on all points on the edges. According to the proportions of eyes width and height

a threshold was given to confirm eyes candidate regions. A trained Support Vector Machine (SVM) classifier was applied to identify eyes regions. Finally

a circle detection operator based on directional Circle Hough Transform (CHT) was used to localize the eyeball center in the regions resulting from SVM classifier. The experiments were carried out on ORL and self-built face database. The results indicate that the method can accomplish the localization in 1.25 s and 3.0 s respectively

while accurate identification can reach 91.6% and 95%

the average localization errors are 7.26 and 3.45 pixels respectively. At the same time

the results also demonstrate that the method is very robust to face's in-plane rotation

scale and facial expression variation.

关键词

Keywords

references

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Related Institution

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