Abstract:
In the last three decades, the eye iris has been investigated as the most unique phenotype feature in the biometric literature. In the iris recognition literature, works achieving outstanding accuracies in very well behavior environments, i.e., when the subject's eye is in a well lightened environment and at a fixed distance for image acquisition, are known since a decade. Nonetheless, in noncooperative environments, where the image acquisition aiming iris location/segmentation is not straightforward, the iris recognition problem has many open issues and has been well researched in recent works. A promising strategy in this context employs a classification approach using multiple signature extraction. Representations are extracted from overlapping and different parts of the iris region. Aiming a robust and noisy invariant classification, an increasing set of acceptance thresholds (frontier) are required for dealing with multiple signatures for iris recognition in the iris matching process. Usually this frontier is estimated using brute force algorithms and a specific step resolution playing an important trade-off between runtime and accuracy of recognition in terms of false acceptance and false rejection rates, measured using half total error rate (HTER). In this sense, this work aims to use Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for finding such frontier. Moreover, we also employ a robust feature extraction technique proposed by us in a previous work. The experiments showed that the use of these evolutionary algorithms provides similar, if not better, effectiveness in very little runtime in a complex database well-known in the literature. Furthermore, it is shown that the frontier obtained by PSO is more stable than the one obtained by GA.
