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Challenging ocular image recognition

机译：富有挑战性的眼图识别

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摘要

Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.
机译：眼部识别是生物统计学研究的一个新领域，旨在克服在存在非理想数据的情况下虹膜识别性能的局限性。扩大虹膜以外的区域具有多个优势，但是还必须解决一些关键问题，例如眼区域的大小，影响性能的因素以及适当地研究这些因素的语料库。在本文中，我们探索并确定了其中一些问题，目的是更好地定义用于眼识别的参数。在现有虹膜和面部数据集上进行虹膜识别方法与纹理和点算子对比的实证研究。实验结果表明，在存在质量较差的虹膜数据的情况下考虑其他功能时，识别性能会显着提高，这为将兴趣扩展到虹膜之外提供了有力的证据。实验还强调了直接收集其他眼部图像的必要性。

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