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

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. (C) 2019 SPIE and IS&T