Hand dorsal vein recognition: Sensor, algorithms and evaluation

Biometric recognition involves in identifying the individual based on behavioural and biological characteristics. Among the various biometric modalities, the dorsal hand vein recognition has been known for high accuracy, stability and resistance to spoofing. The crucial fact, in achieving an accurat...

Full description

Saved in:
Bibliographic Details
Published in:IEEE International Workshop on Imaging Systems and Techniques (IST) pp. 1 - 6
Main Authors: Raghavendra, R., Surbiryala, Jayachander, Busch, Christoph
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2015
Subjects:
Cameras
Feature extraction
Lighting
Magnetic resonance
Pattern recognition
Veins
ISSN:1558-2809
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Biometric recognition involves in identifying the individual based on behavioural and biological characteristics. Among the various biometric modalities, the dorsal hand vein recognition has been known for high accuracy, stability and resistance to spoofing. The crucial fact, in achieving an accurate biometric recognition strongly correlates with the quality of the dorsal hand vein image that can be captured in real-life scenarios. In this paper, we present a new dorsal hand vein sensor that can capture a good quality of dorsal hand vein images. The introduced sensor is based on a near infrared illumination that can emit light in a spectrum of 940nm that in turn is used to illuminate the dorsal hand region. The presented sensor employs a single camera with a simple structure that will further improve the quality of the light to properly illuminate the dorsal hand region. Extensive experiments are carried out on our newly collected database comprised of 50 subjects resulting in 100 unique dorsal hand veins. We also present an extensive evaluation of eight different state-of-the-art techniques that demonstrated the outstanding performance of the Log-Gabor and Sparse Representation Classifier (SRC) with an EER of 0.7%.
ISSN:1558-2809
DOI:10.1109/IST.2015.7294557