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Periocular-based biometrics robust to eye rotation based on polar coordinates

机译:基于极点坐标的基于眼周生物识别技术

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Conventional iris recognition requires a high-resolution camera equipped with a zoom lens and a near-infrared illuminator to observe iris patterns. Moreover, with a zoom lens, the viewing angle is small, restricting the user's head movement. To address these limitations, periocular recognition has recently been studied as biometrics. Because the larger surrounding area of the eye is used instead of iris region, the camera having the high-resolution sensor and zoom lens is not necessary for the periocular recognition. In addition, the image of user's eye can be captured by using the camera having wide viewing angle, which reduces the constraints to the head movement of user's head during the image acquisition. Previous periocular recognition methods extract features in Cartesian coordinates sensitive to the rotation (roll) of the eye region caused by in-plane rotation of the head, degrading the matching accuracy. Thus, we propose a novel periocular recognition method that is robust to eye rotation (roll) based on polar coordinates. Experimental results with open database of CASIA-Iris-Distance database (CASIA-IrisV4) show that the proposed method outperformed the others.
机译:传统的虹膜识别需要配备有变焦镜头和近红外照明器的高分辨率相机来观察虹膜图案。而且,对于变焦镜头,视角小,限制了用户的头部移动。为了解决这些局限性,近来眼周识别已经作为生物特征进行了研究。因为使用了眼睛的较大周围区域而不是虹膜区域,所以对于眼周识别来说,具有高分辨率传感器和变焦镜头的照相机不是必需的。另外,可以通过使用具有宽视角的相机来捕获用户眼睛的图像,这减少了在图像获取期间对用户头部的头部运动的约束。以前的眼周识别方法会提取笛卡尔坐标中的特征,这些特征对由于头部的平面内旋转而引起的眼睛区域的旋转(滚动)敏感,从而降低了匹配精度。因此,我们提出了一种新颖的眼周识别方法,该方法对基于极坐标的眼睛旋转(滚动)具有鲁棒性。开放式CASIA-Iris-Distance数据库(CASIA-IrisV4)数据库的实验结果表明,该方法优于其他方法。

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