Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation

R. Broussard, R. Ives
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引用次数: 20

Abstract

One of the basic challenges to robust iris recognition is iris segmentation. To represent the iris, some researchers fit circles, ellipses or active contours to the boundary pixels of the segmented iris. In order to get an accurate fit, the iris boundary must first be accurately identified. Some segmentation methods operate on a pre-processed gray-scaled image, while others use a thresholded binary edge image. The Hough transform is a popular method used to search for circular or elliptical patterns within the image. Many irises are slightly elliptical, and suffer from eyelid/eyelash occlusion, specular reflections and often the pupil and iris centers are not co-located. Each of these issues can cause a segmentation error. This research uses of a feature saliency algorithm to identify which measurements, used in common iris segmentation methods, jointly contain the most discriminatory information for identify the iris boundary. Once this feature set is identified, an artificial neural network is used to near-optimally combine the segmentation measurements to better localize and identify boundary pixels of the iris. In this approach, no assumption of circularity is assumed when identifying the iris boundary. 322 measurements were tested and eight were found to contain discriminatory information that can assist in identifying the iris boundary. For occluded images, the iris masks created by the neural network were consistently more accurate than the truth mask created using the circular iris boundary assumption.
利用人工神经网络和特征显著性来识别包含最具歧视性信息的虹膜测量值,用于虹膜分割
鲁棒性虹膜识别的基本挑战之一是虹膜分割。为了表示虹膜,一些研究者在分割后的虹膜边界像素上拟合圆、椭圆或活动轮廓。为了获得准确的拟合,必须首先准确地识别虹膜边界。一些分割方法在预处理的灰度图像上操作,而另一些则使用阈值二值边缘图像。霍夫变换是一种常用的方法,用于搜索图像中的圆形或椭圆形图案。许多虹膜有轻微的椭圆,并且受到眼睑/睫毛遮挡、镜面反射的影响,而且瞳孔和虹膜中心经常不在同一位置。这些问题都可能导致分段错误。本研究使用特征显著性算法来识别常用虹膜分割方法中哪些测量值共同包含最具歧视性的信息,以识别虹膜边界。一旦识别出该特征集,利用人工神经网络对分割测量值进行近最优组合,以更好地定位和识别虹膜的边界像素。在这种方法中,在识别虹膜边界时不假设圆度。测试了322个测量值,其中8个发现包含有助于识别虹膜边界的歧视性信息。对于被遮挡的图像,神经网络创建的虹膜掩模始终比使用圆形虹膜边界假设创建的真值掩模更准确。
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