Minutia Matching Using 3d Pore Clouds

This paper proposes a novel methodology for biometric identification of individuals using level-3 features (pores), extracted from 3D fingerprint images obtained through Optical Coherence Tomography (OCT). OCT fingerprint images contain detailed 3D information from both the dermis and the epidermis skin layers of fingertips. Our approach first fetches and extracts pores around minutiae from the 3D fingerprint data, creating small structures called pore clouds. The correspondence of existent pore clouds are then verified for all the three possible fingerprint matching: dermis-dermis, epidermis-epidermis, and dermis-epidermis. To this end, three different measures are extracted and compared: the Hausdorff distance, the Surface Interpenetration Measure and the Root Mean Square Error. Experiments using 518 pore clouds achieved recognition rates of 99.19% for Rank-1 with EER (Equal Error Rate) of 0.72%. From our best knowledge, this is the first time the identification of individuals using only 3D information from pores is explored.