Hand vein recognition based on oriented gradient maps and local feature matching

Di Huang Yinhang Tang Yiding Wang Liming Chen 1 Yunhong Wang
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : The hand vein pattern as a biometric trait for identification has attracted increasing interests in recent years thanks to its properties of uniqueness, permanence, non-invasiveness as well as strong immunity against forgery. In this paper, we propose a novel approach for back of the hand vein recognition. It first makes use of Oriented Gradient Maps (OGMs) to represent the Near-Infrared (NIR) hand vein images, simul-taneously highlighting the distinctiveness of vein patterns and texture of their surrounding corium, in contrast to the state-of-the-art studies that only focused on the segmented vein region. SIFT based local matching is then performed to associate the keypoints between corresponding OGM pairs of the same subject. The proposed approach was benchmarked on the NCUT database consisting of 2040 NIR hand vein images from 102 subjects. The experimental results clearly demonstrate the effectiveness of our approach.
Document type :
Conference papers
Complete list of metadatas
https://hal.archives-ouvertes.fr/hal-01353388
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Thursday, August 11, 2016 - 12:36:43 PM
Last modification on : Wednesday, November 20, 2019 - 2:43:54 AM

Links full text

istex

Identifiers

Collections

CNRS | EC-LYON | UNIV-LYON2 | LIRIS | INSA-LYON | UNIV-LYON1 | INSA-GROUPE | LABEXIMU | UDL

Citation

Di Huang, Yinhang Tang, Yiding Wang, Liming Chen, Yunhong Wang. Hand vein recognition based on oriented gradient maps and local feature matching. The 11th Asian Conference on Computer Vision (ACCV2012), Nov 2012, The Daejeon Convention Center in Daejeon, South Korea. pp.430-444, ⟨10.1007/978-3-642-37447-0_33⟩. ⟨hal-01353388⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

195

Contact

Informations

CCSD