Iris-Biometric Comparators: Minimizing Trade-Offs Costs between Computational Performance and Recognition Accuracy

The intricate structure of the iris constitutes a powerful biometric utilized by iris recognition algorithms to extract discriminative biometric templates. In order to provide a rapid comparison of biometric templates the vast majority of feature extraction methods are designed to generate binary biometric templates, applying the Hamming distance as (dis-)similarity metric. Based on this concept several feature extraction techniques have been proposed in literature, while potential improvements in comparison procedures are commonly neglected. In this paper trade-off costs between the computational performance and recognition accuracy of iris-biometric comparators are investigated. Different comparison techniques of binary biometric templates, and a composition of these, are proposed, where emphasis is put on the trade-off between computational cost and improvement of recognition accuracy, i.e. recognition accuracy is improved at minimal additional computational cost. Experimental results confirm the soundness of the proposed approaches.