应用自适应图像分割与曲线融合的人眼定位

杨帆1; 张华1; 潘国峰1; 王志陶1

doi:10.3788/OPE.20132112.3255

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应用自适应图像分割与曲线融合的人眼定位

信息科学 | 更新时间：2020-08-12

- 应用自适应图像分割与曲线融合的人眼定位
- Eye location based on adaptive image segmentation and curve blending
- 光学精密工程 2013年21卷第12期页码：3255-3262
- 作者机构：
  
  1. 中国人民解放军93716部队，天津 301716
  2. 河北工业大学信息工程学院天津,300401
- 作者简介：
- 基金信息：
  
  国家科技重大专项课题资助项目()
- DOI：10.3788/OPE.20132112.3255
  中图分类号： TP394.1
- 收稿日期：2013-04-23，
  
  修回日期：2013-06-21，
  
  网络出版日期：2013-12-25，
  
  纸质出版日期：2013-12-25
- 稿件说明：
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杨帆张华潘国峰王志陶. 应用自适应图像分割与曲线融合的人眼定位[J]. 光学精密工程, 2013,21(12): 3255-3262

YANG Fan ZHANG Hua PAN Guo-feng WANG Zhi-tao. Eye location based on adaptive image segmentation and curve blending[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3255-3262
杨帆张华潘国峰王志陶. 应用自适应图像分割与曲线融合的人眼定位[J]. 光学精密工程, 2013,21(12): 3255-3262 DOI： 10.3788/OPE.20132112.3255.

YANG Fan ZHANG Hua PAN Guo-feng WANG Zhi-tao. Eye location based on adaptive image segmentation and curve blending[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3255-3262 DOI： 10.3788/OPE.20132112.3255.

摘要

人眼定位采用的传统投影算法对人脸边缘、表情及光照适应性差，易受眉毛、刘海及皱纹的干扰，故本文提出了一种应用自适应图像分割与曲线融合(AISCB)的人眼定位算法。首先

分析了眼睛灰度分布的特殊性，利用特定梯度算子及形态学膨胀元素的方向选择性定义了一种自适应图像分割方法，用于克服边缘及光照干扰；其次，借鉴混合积分投影思想，利用人眼灰度变化和灰度均值双重特点，给出了自适应权值投影曲线融合方法来增强算法的鲁棒性；最后，通过分析融合后曲线特点，分别实现了人眼区域的划分及眼睛精确定位。基于YaleB和JAFFE人脸库的实验表明，评价标准为derr<0.25时，该算法的平均定位正确率达到98.92%。表明AISCB算法对复杂光照及人脸边缘等适应性强，对眉毛、刘海及睫毛等头部部位不敏感，鲁棒性强。

Abstract

Traditional projection algorithms for eye location have a bad adaptation to face edge

expression and illumination

and they are affected by eyebrows

bangs and eye wrinkles easily. Aiming at the above problems

an algorithm of eye location based on Adaptive Image Segmentation and Curve Blending (AISCB) was proposed. Firstly

the specialty of eye gray distribution was analyzed

and an adaptive image segmentation method was defined by using the direction selectivity of gradient operators and certain expansion elements to overcome the influence of edge and illumination. Then

according to the concept of hybrid integral projection and the characteristics of both eye gray changes and gray mean value

the adaptive weight projection curve blending method was used to enhance algorithm's robustness. Finally

the division of face region and the accurate location of eyes were realized through analyzing the characteristics of fused curves. The experiments on YaleB and JAFFE face data libraries show that AISCB algorithm has a good adaptation to complex illumination and face edges

and it is not sensitive to eyebrows

bangs and other head sections. The correct rate of mean location can reach 98.92% when the evaluation criterion is less than 0.25.

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references

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