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

Authors

DOI:

https://doi.org/10.4108/eetiot.v7i27.297

Keywords:

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

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.

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Published

06-04-2022

How to Cite

[1]
J. Xiao, . J. Liu, and H. Liu, “Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment”, EAI Endorsed Trans IoT, vol. 7, no. 27, pp. 1–11, Apr. 2022.