eprintid: 2323
rev_number: 8
eprint_status: archive
userid: 286
dir: disk0/00/00/23/23
datestamp: 2017-01-13 02:36:41
lastmod: 2017-11-29 03:44:17
status_changed: 2017-01-13 02:36:41
type: conference_item
metadata_visibility: show
creators_name: Le, Hong Hai
creators_name: Nguyen, Ngoc Hoa
creators_name: Nguyen, Tri Thanh
creators_id: hoa.nguyen@vnu.edu.vn
creators_id: ntthanh@vnu.edu.vn
title: Automatic Detection of Singular Points in Fingerprint Images using Convolution Neural Networks
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: Minutiae based matching, the most popular approach used in fingerprint matching algorithms, is to calculate the similarity by finding the maximum number of matched minutiae pairs in two given fingerprints. With no prior knowledge about anchor/clue to match, this becomes a combinatorial problem. Global features of the fingerprints (e.g., singular core and delta points) can be used as the anchor to speed up the matching process. Most approaches using the conventional Poincare Index method with additional techniques to improve the detection of the core and delta points. Our approach uses Convolution Neural Networks which gained state-of-the-art results in many computer vision tasks to automatically detect those points. With the experimental results on FVC2002 database, we achieved the accuracy and false alarm of (96%, 7.5%) and (90%, 6%) for detecting core, and delta points, correspondingly. These results are comparative to those of the detection algorithms with human knowledge.
date: 2017
date_type: published
full_text_status: none
pres_type: paper
event_title: The 9th Asian Conference on Intelligent Information and Database Systems
event_location: Kanazawa, Japan
event_dates: 3-5 April 2017
event_type: conference
refereed: TRUE
citation:   Le, Hong Hai and Nguyen, Ngoc Hoa and Nguyen, Tri Thanh  (2017) Automatic Detection of Singular Points in Fingerprint Images using Convolution Neural Networks.  In: The 9th Asian Conference on Intelligent Information and Database Systems, 3-5 April 2017, Kanazawa, Japan.