Iris recognition has emerged as one of the most accurate and convenient biometric for person identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris recognition accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This article presents such a periocular-assisted dynamic framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation, e.g., improvement in the recognition accuracy of 22.9%, 10.4% and 14.6% on three databases under both the verification and recognition scenarios.

中文翻译：

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用于动态虹膜识别的眼周辅助多功能协作


虹膜识别已成为最准确、最方便的个人识别生物识别技术之一，并越来越多地应用于各种电子安全应用中。众所周知，在远处或在较少约束的成像环境下获取的虹膜图像的质量会降低虹膜识别的准确性。眼周信息本质上嵌入在此类虹膜图像中，并且可用于协助此类非理想场景下的虹膜识别。我们对此类虹膜模板的分析还表明感兴趣区域的显着退化和减少，其中虹膜识别可以受益于可以考虑不同二进制位的重要性的相似距离，而不是直接使用文献中的汉明距离。通过结合可用虹膜区域的有效面积的差异，可以动态增强眼周信息，以实现更准确的虹膜识别。本文提出了这样一个眼周辅助动态框架，用于更准确、更少约束的虹膜识别。使用数据集内和跨数据集性能评估在三个公开可用的虹膜数据库上评估该框架的有效性，例如，在验证和识别场景下，三个数据库的识别准确率分别提高了 22.9%、10.4% 和 14.6% 。