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Iris recognition with enhanced depth-of-field image acquisition

机译:虹膜识别,具有增强的景深图像采集

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Automated iris recognition is a promising method for noninvasive verification of identity. Although it is noninvasive, the procedure requires considerable cooperation from the user. In typical acquisition systems, the subject must carefully position the head laterally to make sure that the captured iris falls within the field-of-view of the digital image acquisition system. Furthermore, the need for sufficient energy at the plane of the detector calls for a relatively fast optical system which results in a narrow depth-of-field. This latter issue requires the user to move the head back and forth until the iris is in good focus. In this paper, we address the depth-of-field problem by studying the effectiveness of specially designed aspheres that extend the depth-of-field of the image capture system. In this initial study, we concentrate on the cubic phase mask originally proposed by Dowski and Cathey. Laboratory experiments are used to produce representative captured irises with and without cubic asphere masks modifying the imaging system. The iris images are then presented to a well-known iris recognition algorithm proposed by Daugman. In some cases we present unrestored imagery and in other cases we attempt to restore the moderate blur introduced by the asphere. Our initial results show that the use of such aspheres does indeed relax the depth-of-field requirements even without restoration of the blurred images. Furthermore, we find that restorations that produce visually pleasing iris images often actually degrade the performance of the algorithm. Different restoration parameters are examined to determine their usefulness in relation to the recognition algorithm.
机译:自动化虹膜识别是一个有希望的非识别身份验证的方法。虽然它是非侵入性的,但程序需要与用户相当合作。在典型的采集系统中,主题必须仔细地将磁头定位,以确保捕获的虹膜落入数字图像采集系统的视野中。此外,在检测器的平面上需要足够的能量来调用相对快速的光学系统,从而导致狭义的景深。后一种问题要求用户来回移动头部,直到虹膜处于良好的焦点之前。在本文中,我们通过研究延长图像捕获系统的景深的特殊设计的非球体的有效性来解决景深问题。在这个初步研究中,我们专注于最初由Dowski和Catey提出的立方相位面具。实验室实验用于生产具有和无需修改成像系统的立方中间口罩的代表捕获的虹膜。然后将虹膜图像呈现给Daugman提出的众所周知的虹膜识别算法。在某些情况下,我们展示了未经讨论的图像,并且在其他情况下,我们试图恢复由购物中心引入的中等模糊。我们的初步结果表明,即使没有恢复模糊的图像,也确实放宽了现场深度要求。此外,我们发现在视觉上令人愉悦的虹膜图像中产生的修复体通常实际降低了算法的性能。检查不同的恢复参数以确定其与识别算法有关的实用性。

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