  With the development of high-resolution fingerprint scanners, high-resolution
fingerprint-based biometric recognition has received increasing attention in
recent years. This paper presents a pore feature-based approach for biometric
recognition. Our approach employs a convolutional neural network (CNN) model,
DeepResPore, to detect pores in the input fingerprint image. Thereafter, a
CNN-based descriptor is computed for a patch around each detected pore.
Specifically, we have designed a residual learning-based CNN, referred to as
PoreNet that learns distinctive feature representation from pore patches. For
verification, the match score is generated by comparing pore descriptors
obtained from a pair of fingerprint images in bi-directional manner using the
Euclidean distance. The proposed approach for high-resolution fingerprint
recognition achieves 2.91% and 0.57% equal error rates (EERs) on partial (DBI)
and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most
importantly, it achieves lower FMR1000 and FMR10000 values than the current
state-of-the-art approach on both the datasets.

Computer Vision and Pattern Recognition