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arXiv · 1908.04842

SP-NET: One Shot Fingerprint Singular-Point Detector

Geetika Arora·Ranjeet Ranjan Jha·Akash Agrawal·Kamlesh Tiwari·Aditya Nigam
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Abstract

Singular points of a fingerprint image are special locations having high curvature properties. They can play a pivotal role in fingerprint normalization and reliable feature extraction. Accurate and efficient extraction of a singular point plays a major role in successful fingerprint recognition and indexing. In this paper, a novel deep learning based architecture is proposed for one shot (end-to-end) singular point detection from an input fingerprint image. The model consists of a Macro-Localization Network and a Micro-Regression Network along with three stacked hourglass as a bottleneck. The proposed model has been tested on three databases viz. FVC2002 DB1_A, FVC2002 DB2_A and FPL30K and has been found to achieve true detection rate of 98.75%, 97.5% and 92.72% respectively, which is better than any other state-of-the-art technique.

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Geetika Arora, Ranjeet Ranjan Jha, Akash Agrawal, Kamlesh Tiwari, Aditya Nigam. 2019-08-13. SP-NET: One Shot Fingerprint Singular-Point Detector. https://arxiv.org/abs/1908.04842

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