Pore detection in high-resolution fingerprint images using deep residual network

Vijay Anand; Vivek Kanhangad

doi:10.1117/1.JEI.28.2.020502

1 April 2019 Pore detection in high-resolution fingerprint images using deep residual network

Vijay Anand, Vivek Kanhangad

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 020502 (April 2019). https://doi.org/10.1117/1.JEI.28.2.020502

Abstract

We present a residual learning-based convolutional neural network, referred to as DeepResPore, for detection of pores in high-resolution fingerprint images. Specifically, the proposed DeepResPore model generates a pore intensity map from the input fingerprint image. Subsequently, the local maxima filter is operated on the pore intensity map to identify the pore coordinates. The results of our experiments indicate that the proposed approach is effective in extracting pores with a true detection rate of 94.49% on test set I and 93.78% on test set II of the publicly available PolyU HRF dataset at a false detection rate of 8.5%. Most importantly, the proposed approach achieves state-of-the-art performance on both test sets.

Citation Download Citation

Vijay Anand and Vivek Kanhangad "Pore detection in high-resolution fingerprint images using deep residual network," Journal of Electronic Imaging 28(2), 020502 (1 April 2019). https://doi.org/10.1117/1.JEI.28.2.020502

Received: 8 December 2018; Accepted: 19 March 2019; Published: 1 April 2019

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