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Daugman&#x2019;s design of IrisCode continues to be fascinating in the research world with its practicality, efficiency, and outstanding performance. The limits of Daugman&#x2019;s recognition system, however, remain a topic of active discussion. Multiple approaches to scale performance have been explored in the past. Despite them, the problem of finding the maximal population of IrisCode remains open. Because of this, we appeal to Rate-Distortion theory (limits of error-correction codes) to establish bounds on the maximum possible population of iris classes that IrisCode can support under the constraint of a minimum Hamming Distance (HD) between any two codewords. This approach considers the distribution of iris data within and across iris classes and the quality of iris data. We first present the Hamming, Plotkin, and Elias-Bassalygo upper bounds and the Gilbert-Varshamov lower bound on the population of IrisCode. The bounds relate the number of iris classes that the IrisCode algorithm can sustain and the quality of iris data expressed in terms of HD. Then, we analyze our results and draw conclusions regarding the relationship of IrisCode population size and the level of quality that enrolled data must have to ensure a particular population coverage. By applying the theory presented here, researchers can better understand what maximum population is achievable based on the quality of their iris dataset.

New Perspectives on Recognition Performance of Daugman’s IrisCode or “Everything is New–it is Well Forgotten Old”