IoT 22(27): e5

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.