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A Highly Efficient Biometrics Approach for Unconstrained Iris Segmentation and Recognition

机译:一种用于无约束虹膜分割和识别的高效生物特征识别方法

摘要

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineats the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.
机译:本文为虹膜生物特征识别的约束开发了一种创新的方法。这项研究工作有两个主要贡献:(1)设计了一种屡获殊荣的分割算法,该算法在约束较小的环境中进行了捕获,该图像捕获是在移动对象的可见光条件下进行的,并且(2)开发了一种开拓性的算法。虹膜生物特征识别方法基于在不同采集情况下移动人员的视频,结合虹膜的分割和识别。论文的第一部分介绍了一种鲁棒且快速的分割方法,该方法使用了UBIRIS(版本2)带噪虹膜数据库中包含的静态图像。结果表明,使用UBIRIS.v2部分数据库中的500张随机选择的图像时,估计的准确性为98%,而在一个涉及全球35个国家/地区的97名参与者的国际比赛中,“嘈杂的虹膜挑战评估(NICE.I)”估计为97%。 ,将该研究小组排名第六。通过接近实时的处理速度可以达到这种精度。本文的第二部分提出了一种创新的基于视频虹膜图像的分割与识别方法。在通过一种新颖的分割策略来描绘虹膜区域的分割阶段之后,已经完成了对约束较少的视频虹膜生物特征识别阶段的一些开创性实验。在基于视频的约束较少的虹膜识别中,测试或主题虹膜视频/图像以及已注册的虹膜图像是使用不同的采集系统采集的。在匹配步骤中,通过比较来自测试图像的编码签名与来自注册虹膜图像的每个签名数据集的相似距离,来完成验证/识别结果。通过获得的改进,结果证明了在更具挑战性的不受约束的环境下的结果是高度准确的。这为85位测试用户提供了305个测试图像,从而导致0%的错误接受率(FAR)和17.64%的错误拒绝率(FRR),这对虹膜生物特征识别研究和系统设计。

著录项

  • 作者

    Chen Yu;

  • 作者单位
  • 年度 2010
  • 总页数
  • 原文格式 PDF
  • 正文语种
  • 中图分类
  • 入库时间 2022-08-20 21:11:29

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