Authors:
Arathi Arakala
;
Hao Hao
;
Stephen Davis
and
K. J. Horadam
Affiliation:
RMIT University, Australia
Keyword(s):
Biometric Graph, Graph Matching, Authentication, Palm Vein.
Related
Ontology
Subjects/Areas/Topics:
Access Control
;
Data Engineering
;
Databases and Data Security
;
Feature Selection and Extraction
;
Information and Systems Security
;
Internet Technology
;
Pattern Recognition
;
Theory and Methods
;
Web Information Systems and Technologies
Abstract:
We present a graphical representation for palm vein patterns for use as biometric identifiers. The palm vein
image captured from an infra red camera is converted into a spatial graph. After image enhancement and
binarisation, the palm vein features are extracted from the skeleton using a novel two stage spur removal
technique. The location of the features and the connections between them are used to define a Palm Vein
Graph. Palm vein graphs are compared using the Biometric Graph Matching (BGM) Algorithm. We propose
a graph registration algorithm that improves over existing state of the art algorithms for graph registration.
We introduce 10 graph topology-based measures for comparing palm vein graphs. Experiments are conducted
on a public palm vein database. One of the introduced measures, an edge-based similarity, gave a definite
improvement in matching accuracies over other published results on the same database, especially for samples
with only a small common overlap area due to d
isplacement. In addition, when the edge-based measure was
combined with one of three other topological features, we demonstrate a further improvement in matching
accuracy.
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