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Iris recognition is a potential tool in secure personal identification and authentication in view of properties such as uniqueness, non-invasiveness and stability of human iris patterns. A new approach based on the Hausdorff distance measure is proposed for iris recognition. In contrast to existing approaches that consider grey or colour images, the new approach considers the binary edge maps of irises. Edge maps have advantages in terms of low storage space, fast transmission, fast processing and hardware compatibility. A new measure, called local partial Hausdorff distance, is computed between the binary edge maps of normalised iris images. The proposed dissimilarity measure has been tested on the high-quality UPOL iris images captured in a constrained environment. The recognition performance of the proposed method has been studied for different values of parameters such as block size and partialness. An appropriate choice of these parameters achieves a recognition rate of more than 98%. The results demonstrate the significance of linear features in the iris edge maps in discriminating different irises.