NDLI: Self-geometric relationship filter for efficient SIFT key-points matching in full and partial palmprint recognition

Content Provider	IET Digital Library
Author	Almaghtuf, Jumma Khelifi, Fouad
Abstract	Recently, palmprints have been broadly reported in the literature as an effective biometric modality. Although scale-invariant feature transform (SIFT)-based features have been proven to be robust against image transformations and deformations, SIFT has not been as successful as other methods in palmprint recognition. In fact, SIFT-based identification has been widely criticised in biometrics due to its high false matching rate. To overcome this weakness, a new filtering method for SIFT-based palmprint matching, called the self-geometric relationship-based filter (SGR-filter) is presented. While existing SIFT matching considers only the relationship between the SIFT points of the query image, on one hand, and their corresponding points in the reference image, on the other hand, SGR-filtering further takes into account the geometric relationship between SIFT points within the query image in comparison with the relationship of the corresponding matched points in the reference image. Assessed with the proposed SGR-filter on various datasets, the SIFT-based palmprint recognition system has been shown to deliver significantly higher performance when compared with the conventional SIFT matching as well as another related key-points filtering technique. Furthermore, experimental results on a number of different full and partial palmprint datasets have shown the superiority of the proposed system over state-of-the-art techniques.
Starting Page	296
Ending Page	304
Page Count	9
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 4, Jul (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0148
Journal	IET Biometrics
Publisher Date	2018-01-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biomimetics Computer Vision And Image Processing Technique Conventional SIFT Matching Corresponding Matched Points Corresponding Points Different Full Palmprint Datasets Effective Biometric Modality Efficient SIFT Key-points Existing SIFT Matching Feature Extraction Filtering Method Geometric Deformation Geometric Relationship Filter High False Matching Rate Image Matching Image Recognition Intra-class Variations Optical, Image And Video Signal Processing Palmprint Matching Palmprint Recognition Palmprint Recognition Reside Palmprint Recognition System Palmprints Partial Image Access Partial Palmprint Datasets Partial Palmprint Recognition Query Image Reference Image Related Key-Point Filtering Technique Scale-invariant Feature Self-geometric Relationship SGR-filter SGR-filtering SIFT Point Touchless Image Acquisition Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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