The assessment of iris uniqueness plays a crucial role in analyzing the capabilities and limitations of iris recognition systems. Among the various methodologies proposed, Daugman's approach to iris uniqueness stands out as one of the most widely accepted. According to Daugman, uniqueness refers to the iris recognition system's ability to enroll an increasing number of classes while maintaining a near-zero probability of collision between new and enrolled classes. Daugman's approach involves creating distinct IrisCode templates for each iris class within the system and evaluating the sustainable population under a fixed Hamming distance between codewords. In our previous work [23], we utilized Rate-Distortion Theory (as it pertains to the limits of error-correction codes) to establish boundaries for the maximum possible population of iris classes supported by Daugman's IrisCode, given the constraint of a fixed Hamming distance between codewords. Building upon that research, we propose a novel methodology to evaluate the scalability of an iris recognition system, while also measuring iris quality. We achieve this by employing a sphere-packing bound for Gaussian codewords and adopting a approach similar to Daugman's, which utilizes relative entropy as a distance measure between iris classes. To demonstrate the efficacy of our methodology, we illustrate its application on two small datasets of iris images. We determine the sustainable maximum population for each dataset based on the quality of the images. By providing these illustrations, we aim to assist researchers in comprehending the limitations inherent in their recognition systems, depending on the quality of their iris databases.

翻译：虹膜唯一性评估对于分析虹膜识别系统的能力与局限性具有关键作用。在多种已提出的方法中，Daugman的虹膜唯一性分析方法是目前最广泛接受的方法之一。根据Daugman的定义，唯一性指虹膜识别系统在维持新注册类别与已注册类别间冲突概率趋近于零的前提下，支持不断增加类别数量的能力。其方法涉及为系统内每个虹膜类别生成独立的IrisCode模板，并在码字间固定汉明距离约束下评估可持续承载的类别规模。在前期工作[23]中，我们运用率失真理论（针对纠错码极限特性）推导了Daugman IrisCode在固定码字间汉明距离条件下所支持的最大虹膜类别数量边界。基于该研究，我们提出了一种新型方法论，可在评估虹膜质量的同时衡量虹膜识别系统的可扩展性。通过采用高斯码字的球填充界，并借鉴Daugman将相对熵作为虹膜类别间距离度量的方法，我们验证了该方法的有效性。为展示所提方法的实用性，我们在两个小型虹膜图像数据集上进行了应用，根据图像质量确定了各数据集可持续承载的最大类别规模。通过这些实例分析，我们旨在帮助研究人员根据其虹膜数据库的质量特性，深入理解识别系统固有的性能局限性。