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An efficient iris segmentation model based on eyelids and eyelashes detection in iris recognition system

机译:虹膜识别系统中基于眼睑和睫毛检测的有效虹膜分割模型

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This paper presents an efficient noise reduction scheme to remove localized high frequency information from segmented iris region for personal authentication based on radial suppression. Eyelash and eyelids of localized iris area is considered as noisy information. Accuracy of iris recognition system generally depends on accurate segmentation and noise deduction. Proposed method not only removes eyelash and eyelids by suppressing localized frequency using radial suppression but also detects the pupil and iris center accurately as well as localizes the iris and pupil region. Finally, this paper also designates a prototype of automated iris recognition system for personnel authentication. For iris feature extraction purpose, one dimensional Log Gabor wavelet has been used where the feature vector length has been reduced without less loss of Information. It has also been showed here that the proposed detection model has a stable matching score. The proposed automated iris recognition system with maximum suppression of eyelashes has less equal error rate which indicates the superiority of the performance compared to the other existing methods.
机译:本文提出了一种有效的降噪方案,可以从分割的虹膜区域中去除局部高频信息,以基于径向抑制进行个人认证。睫毛和眼睑局部虹膜区域被认为是嘈杂的信息。虹膜识别系统的准确性通常取决于准确的分割和降噪。提出的方法不仅通过使用径向抑制来抑制局部频率来去除睫毛和眼睑,而且还可以准确地检测瞳孔和虹膜中心以及对虹膜和瞳孔区域进行定位。最后,本文还设计了一种用于人员身份验证的自动虹膜识别系统的原型。为了提取虹膜特征,已使用一维Log Gabor小波,其中特征矢量的长度已减小,而信息丢失较少。在此还表明,所提出的检测模型具有稳定的匹配分数。所提出的具有最大程度抑制睫毛的自动虹膜识别系统具有较少的相等错误率,这表明与其他现有方法相比,该方法具有优越的性能。

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