Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment

Jianyu Xiao; Jiuan Liu; Huanhua Liu

Research Article

Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment

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@ARTICLE{10.4108/eetiot.v7i27.297,
    author={Jianyu Xiao and Jiuan Liu and Huanhua Liu},
    title={Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment},
    journal={EAI Endorsed Transactions on Internet of Things},
    volume={7},
    number={27},
    publisher={EAI},
    journal_a={IOT},
    year={2022},
    month={4},
    keywords={Fingerprint recognition, Internet of thing, ORB, Feature point extraction and matching, Feature fusion},
    doi={10.4108/eetiot.v7i27.297}
}

Jianyu Xiao
Jiuan Liu
Huanhua Liu
Year: 2022
Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment
IOT
EAI
DOI: 10.4108/eetiot.v7i27.297

Jianyu Xiao¹, Jiuan Liu¹, Huanhua Liu²^,*

1: School of Computer Science and Engineering, Central South University, Changsha 410075, China
2: School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China

*Contact email: liuhuanhua@hufe.edu.cn

Abstract

Most of the fingerprint matching algorithms were proposed for large area fingerprints, which can hardly work effectively in small-area fingerprints. In this work, an improved ORB algorithm is proposed for small-area fingerprint matching in embedded mobile devices. In feature descriptor design, we analyzed the characters of the fingerprint in the embedded mobile devices and discard the multi-scale feature process to reduce the amount of operations. Moreover, we proposed a fusion descriptor combing LBP and rBRIEF descriptor. In the key point matching process, we proposed a two-step (coarse and fine) matching method by using Hamming distance and cosine similarity, respectively. The experimental results show that the proposed method has a rejection rate of 6.4%, a false recognition rate of 0.1%, and an average matching time of 58ms. It can effectively improve the performance of small-area fingerprint matching and meet the application requirements of embedded mobile device authentication.

Keywords: Fingerprint recognition, Internet of thing, ORB, Feature point extraction and matching, Feature fusion

Received: 2022-02-21
Accepted: 2022-03-08
Published: 2022-04-06
Publisher: EAI

: http://dx.doi.org/10.4108/eetiot.v7i27.297

Copyright © 2022 Jianyu Xiao et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.