2018
Vasilopoulos, Christos; Skodras, Athanassios
A Novel Finger Vein Recognition System Based on Enhanced Maximum Curvature Points Proceedings Article
In: 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1-5, 2018.
Abstract | Links | BibTeX | Tags: biometrics, Enhanced Maximum Curvature Points, feature extraction, finger vein recognition, Personal identification
@inproceedings{Vasilopoulos2018,
title = {A Novel Finger Vein Recognition System Based on Enhanced Maximum Curvature Points},
author = {Christos Vasilopoulos and Athanassios Skodras},
doi = {10.1109/IVMSPW.2018.8448746},
year = {2018},
date = {2018-06-01},
booktitle = {2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
pages = {1-5},
abstract = {Finger vein recognition is a biometric method of authentication that offers high security, efficiency and stability. In this paper we propose a new finger vein recognition system that utilizes the Enhanced Maximum Curvature Points (EMC) technique for finger vein pattern extraction and introduces a new pre-processing stage. In addition, it combines two matching methods, leading to better recognition performance in terms of EER, FAR, FRR and recognition rate than other methods. We present the experimental results obtained by applying our system on the databases SDUMLA-HMT, Tsingua, FV-USM and HKPU and compare them with similar approaches applied on these databases.},
keywords = {biometrics, Enhanced Maximum Curvature Points, feature extraction, finger vein recognition, Personal identification},
pubstate = {published},
tppubtype = {inproceedings}
}
Finger vein recognition is a biometric method of authentication that offers high security, efficiency and stability. In this paper we propose a new finger vein recognition system that utilizes the Enhanced Maximum Curvature Points (EMC) technique for finger vein pattern extraction and introduces a new pre-processing stage. In addition, it combines two matching methods, leading to better recognition performance in terms of EER, FAR, FRR and recognition rate than other methods. We present the experimental results obtained by applying our system on the databases SDUMLA-HMT, Tsingua, FV-USM and HKPU and compare them with similar approaches applied on these databases.