Paper Info
Title
Robust Ear Recognition Using Gradient Ordinal Relationship Pattern
Abstract
A reliable personal recognition based on ear biometrics is highly in demand due to its vast application in automated surveillance, law enforcement etc. In this paper a robust ear recognition system is proposed using gradient ordinal relationship pattern. A reference point based normalization is proposed along with a novel ear transformation over normalized ear, to obtain robust ear representations. Ear samples are enhanced using a local enhancement technique. Later a dissimilarity measure is proposed that can be used for matching ear samples. Two publicly available ear databases IITD and UND-E are used for the performance analysis. The proposed system has shown very promising results and significant improvement over the existing state of the art ear systems. The proposed system has shown robustness against small amount of illumination variations and affine transformations due to the virtue of ear transformation and tracking based matching respectively.
Year
DOI
Venue
2014
10.1007/978-3-319-16634-6_45
COMPUTER VISION - ACCV 2014 WORKSHOPS, PT III
Field
DocType
Volume
Affine transformation,Ear recognition,Computer vision,Normalization (statistics),Pattern recognition,Computer science,Ordinal number,Robustness (computer science),Artificial intelligence,Biometrics,Optical flow
Conference
9010
ISSN
Citations 
PageRank 
0302-9743
1
0.36
References 
Authors
0
2
Name
Order
Citations
PageRank
Aditya Nigam115428.82
Phalguni Gupta280582.58