Paper Info
Title
Palmprint Recognition Based On Harris Corner Detection And Statistical Offset Matching
Abstract
Palmprint deformation, including rotation, displacement, swelling and so on, is a crucial factor for the palmprint recognition performance. The traditional algorithms mainly focus on the feature-based matching and the block-based matching. In this paper, a novel block-based matching palmprint recognition scheme is proposed. The sampling points on the template image are built by the harris corner detection algorithm. Furthermore, the corresponding sampling points on the query palmprint image are estimated by image pyramids algorithm for reducing the impact of distortion. At last, a matching method based on statistical offset is proposed to calculate the final matching score. Extensive experiments on public palmprint database shows that our method can achieve excellent recognition performance compared with many other classic methods.
Year
Venue
Keywords
2017
2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI)
Plmprint Recognition, Image Pyramids, Harris Corner Detection
Field
DocType
Citations 
Computer vision,Pattern recognition,Corner detection,Computer science,Image coding,Feature extraction,Artificial intelligence,Sampling (statistics),Distortion,Offset (computer science)
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Hua Wang121452.30
Kai Xiao2146.10
Weiqiang Zhao301.01
Liaojun Pang419824.11
Heng Zhao5325.34