Open AccessSmall-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment
Author(s) -
Jianyu Xiao,
Jiuan Liu,
Huanhua Liu
Publication year - 2022
Publication title -
eai endorsed transactions on internet of things
Language(s) - English
Resource type - Journals
ISSN - 2414-1399
DOI - 10.4108/eetiot.v7i27.297
Subject(s) - fingerprint (computing) , computer science , hamming distance , fingerprint recognition , matching (statistics) , pattern recognition (psychology) , orb (optics) , artificial intelligence , process (computing) , feature (linguistics) , blossom algorithm , similarity (geometry) , computer vision , algorithm , mathematics , image (mathematics) , linguistics , statistics , philosophy , operating system
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.