Accurate eye location in near-infrared images based on ellipse fitting
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摘要: 提出了一种新的近红外人脸图像的眼睛精确定位方法. 该方法首先使用基于Haar特征和AdaBoost算法的人脸检测分类器确定人脸区域和初始眼睛位置;然后用Sobel算子对眼睛区域进行边缘检测处理,得到眼睛边缘,并对它进行椭圆拟合获得眼睛的椭圆轮廓线;最后把拟合椭圆的中心点作为眼睛的精确位置. 实验表明,在正面人脸情况下,本方法能精确地定位近红外人脸图像的眼睛位置,在归一化人脸为120120像素时,其平均误差小于1.5个像素,处理时间约7 ms.
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关键词:
- 人脸检测 /
- 眼睛定位 /
- 近红外图像 /
- AdaBoost算法 /
- 椭圆拟合
Abstract: This paper presents a novel approach to precisely locate eye position in near-infrared facial images. In this approach, we first determine the face region and initial eye position using face detection classifier based on Haar features and AdaBoost algorithm. Then we detect the eye edge in the eye region using Sobel operator, fit it into an elliptical contour. Finally, the center point of eye is located by the center of the fitted ellipse. With 120120 normalized face images, the experiments show that the proposed approach is accurate. The average error is less than 1.5 pixels and the processing time is about 7 ms.-
Key words:
- face detection /
- eye location /
- NIR image /
- AdaBoost /
- ellipse fitting
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[1] [1] VIOLA P, JONES M. Robust Real-Time Face Detection[J]. International Journal of Computer Vision, 2004,57: 137-154.[2] 王基帆, 童卫青. 基于数理形态学的近红外光图像实时人脸检测[J]. 华东师范大学学报: 自然科学版, 2010,(3): 39-47.[3] 张昌明, 童卫青, 王燕群. 一种新型的高性能近红外光人脸检测和眼睛定位算法[J]. 计算机系统应用, 2010, 20: 96-101.[4] 薛程, 王士同. 一种新的不基于Hough变换的随机椭圆检测算法[J]. 微计算机信息, 2006, 22:265-268.[5] FITZGIBBON A W, PILU M, FISHER R B. Direct least squares fitting of ellipses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21: 476-480.[6] 丘维声. 解析几何[M]. 北京: 北京大学出版社, 1999: 149-160.[7] 闫蓓, 王斌, 李媛. 基于最小二乘法的椭圆拟合改进算法[J]. 北京航空航天大学学报, 2008, 34: 295-298.
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