The use of sweat pores in fingerprint recognition is becoming increasingly popular, mostly because of the wide availability of pores, which provides complementary information for matching distorted or incomplete images. In this work we present a fully automatic pore-based fingerprint recognition framework that combines both pores and ridges to measure the similarity of two images. To obtain the ridge structure, we propose a novel pore-based ridge reconstruction approach by considering a connect-the-dots strategy. To this end, Kruskal's minimum spanning tree algorithm is employed to connect consecutive pores and form a graph representing the ridge skeleton. We evaluate our framework on the PolyU HRF database, and the obtained results are favorably compared to previous results in the literature.

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The IMAGO Research Group was founded in 1996 and accumulated great expertise in range image analysis during its existence. The group has been working on projects related to computer vision, image processing and computer graphics in order to produce solutions for different areas, such as culture, education, social inclusion, health and security. The efforts of the group are currently focused on digital preservation and biometric recognition, in which there are three main ongoing projects: 3D Virtual Museum, 3D Face Recognition and Newborn Identification. These projects have virtual reality and 3D interaction features beyond the HCI requirements, that provides motivation in the use of our systems.