Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint
  Recognition   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.

16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol