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Detection of eyes from human faces by Hough transform and separability filter

机译:通过Hough变换和可分离性过滤器从人脸检测眼睛

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We propose a new algorithm to detect the irises of both eyes from a human face in an intensity image. Using the separability filter proposed by Fukui et al., the algorithm first extracts intensity valleys, which we call blobs in this paper, as the candidates for the irises. Next, for each pair of blobs, the algorithm computes a cost using the Hough transform and separability filter to measure the fit of the pair of blobs to the image. Then, the algorithm selects a pair of blobs with the smallest cost as the irises of both eyes. As the result of the experiment using all faces without spectacles in the face database of the University of Bern, the success rate of the proposed algorithm was 96.5% on the average.
机译:我们提出了一种新算法,可从强度图像中检测人脸的两只眼睛的虹膜。使用Fukui等人提出的可分离性滤波器,该算法首先提取强度谷(在本文中称为斑点)作为虹膜的候选对象。接下来,对于每对斑点,该算法使用霍夫变换和可分离性过滤器来计算这对斑点对图像的拟合度,从而计算出成本。然后,该算法选择代价最小的一对斑点作为两只眼睛的虹膜。在伯尔尼大学的人脸数据库中使用所有没有眼镜的人脸进行实验的结果表明,该算法的平均成功率为96.5%。

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