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

机译:基于眼睑和睫毛检测的虹膜识别系统的高效虹膜分割模型

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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:本文提出了一种高效的降噪方案,以基于径向抑制将局部高频信息从分段虹膜区域移除局部化的虹膜区域。局部虹膜区域的睫毛和眼睑被认为是嘈杂的信息。虹膜识别系统的准确性通常取决于精确的分割和噪声扣除。所提出的方法不仅通过使用径向抑制来抑制局部频率,而且还可以准确地检测瞳孔和虹膜中心,以及定位虹膜和瞳孔区域来除去睫毛和眼睑。最后,本文还指定了用于人事认证的自动虹膜识别系统的原型。对于虹膜特征提取目的,已经使用了一维对数Gabor小波,其中特征向量长度已经减小而不减少信息损失。这里还显示出所提出的检测模型具有稳定的匹配分数。所提出的自动虹膜识别系统具有最大抑制睫毛的误差率较小,表示与其他现有方法相比的性能的优势。

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