A Deep Learning based Approach for Biometric Recognition using Hybrid Features

Mrityunjay Kumar, Arvind Kumar Tiwari

2021

Abstract

Biometric authentication and identification is most important and challenging problem in this evolved era of computer technology. Goal of new technical developments is to make our task easy and life smoother. It is important to develop an efficient computational method to recognize and identify biometrics more efficiently with least time delay. This paper proposed a CNN based multimodal biometric identification system using feature fusion of three biometric traits Faces, Fingerprints, and Iris. In this paper PCA and WT are used for feature extraction and feature fusion respectively. The accuracy of the proposed approach is about 96.67% on fused features of three biometric traits Faces, Fingerprints, and Iris. The proposed approach in this paper provides better accuracy in compare to the existing method in literature.

Paper Citation

in Harvard Style

Kumar M. and Tiwari A. (2021). A Deep Learning based Approach for Biometric Recognition using Hybrid Features. In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE, ISBN 978-989-758-544-9, pages 273-282. DOI: 10.5220/0010567900003161

in Bibtex Style

@conference{icacse21,
author={Mrityunjay Kumar and Arvind Kumar Tiwari},
title={A Deep Learning based Approach for Biometric Recognition using Hybrid Features},
booktitle={Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,},
year={2021},
pages={273-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010567900003161},
isbn={978-989-758-544-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,
TI - A Deep Learning based Approach for Biometric Recognition using Hybrid Features
SN - 978-989-758-544-9
AU - Kumar M.
AU - Tiwari A.
PY - 2021
SP - 273
EP - 282
DO - 10.5220/0010567900003161