A Revised Eye-Localization Algorithm Based on Random Forest

MA Yue; HE Guang-hui

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A Revised Eye-Localization Algorithm Based on Random Forest

更新时间：2026-09-04

- A Revised Eye-Localization Algorithm Based on Random Forest
- Vol. 33, Issue 7, Pages: 1-4(2016)
- 作者机构：
  
  上海交通大学, 微电子学院,上海,200240
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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马越, 贺光辉. 一种改进的基于随机森林的快速人眼定位方法[J]. 微电子学与计算机, 2016, 33(7): 1-4, 10.

MA Yue, HE Guang-hui. A Revised Eye-Localization Algorithm Based on Random Forest[J]. 2016, 33(7): 1-4.
马越, 贺光辉. 一种改进的基于随机森林的快速人眼定位方法[J]. 微电子学与计算机, 2016, 33(7): 1-4, 10. DOI：

MA Yue, HE Guang-hui. A Revised Eye-Localization Algorithm Based on Random Forest[J]. 2016, 33(7): 1-4. DOI：

摘要

为了得到更强的鲁棒性和更好的实时性

针对精确的人眼定位提出了基于随机森林的自适应梯度提升算法(Adaptive Gradient Boosting Random Forest)

同时引入了更加多样的训练样本来降低诸如光线强弱

旋转角度等外部环境对定位的影响.在BioID数据库中

此方法的准确率为92%

定位时间少于1 ms.实验结果表明

它已经满足在大多数不可控环境对鲁棒性和实时性的要求.

Abstract

In this paper

we present an enhanced Random Forest(RF) model for precise eye localization. To extend Random Forest

we 1) propose the randomized trees with adaptive gradient boosting for a more accurate eye localization 2) introduce a series of standard samples with random perturbation for the robustness to changes in illumination and head pose and eye rotation. Performance results of our methods showed that it can obtain an accuracy of 92 percent on BioID database and gives a frame processing time of less than 1ms because of the low computation cost. Experimental results on the challenging BioID database show that our model can locate eyes accurately and efficiently under a broad range of uncontrolled variations involving lightings

camera qualities

occlusions

etc.

关键词

Keywords

references

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Kroon B, Hanjalic A, Maas S M. Eye localization for face matching: is it always useful and under what conditions[C]//Proceedings of the 2008 international conference on Content-based image and video retrieval. Canada, Niagara Falls, 2008: 379-388.

Yi D, Lei Z, Li S Z. A robust eye localization method for low quality face images[C]//Biometrics (IJCB), 2011 International Joint Conference on. USA, Washington DC, 2011: 1-6.

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Fasel I, Fortenberry B, Movellan J. A generative framework for real time object detection and classification[J]. Computer Vision and Image Understanding, 2005, 98(1): 182-210. DOI: 182

Asadifard M, Shanbezadeh J. Automatic adaptive center of pupil detection using face detection and cdf analysis[C]//Proceedings of the International MultiConference of Engineers and Computer Scientists. China, Hongkong, 2010: 3-5.

Valenti R, Gevers T. Accurate eye center location and tracking using isophote curvature[C]//Computer Vision and Pattern Recognition, CVPR 2008. IEEE Conference on. USA, Anchorage, Alaska, 2008.

Peng K. A robust agorithm for eye detection on gray intensity face without spectacles[J]. Journal of Computer Science % Technology, 2005, 5(3): 127-133. DOI: 127

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