Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23101
Title: Periocular recognition using unsupervised convolutional RBM feature learning
Authors: Nie, L
Kumar, A
Zhan, S
Keywords: Biometrics
CRBM
Periocular Recognition
Supervised Metric learning
Unsupervised Feature Learning
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - International Conference on Pattern Recognition, 2014, 6976788, p. 399-404 How to cite?
Abstract: Automated and accurate biometrics identification using periocular imaging has wide range of applications from human surveillance to improving performance for iris recognition systems, especially under less-constrained imaging environment. Restricted Boltzmann Machine is a generative stochastic neural network that can learn the probability distribution over its set of inputs. As a convolutional version of Restricted Boltzman Machines, CRBM aim to accommodate large image sizes and greatly reduce the computational burden. However in the best of our knowledge, the unsupervised feature learning methods have not been explored in biometrics area except for the face recognition. This paper explores the effectiveness of CRBM model for the periocular recognition. We perform experiments on periocular image database from the largest number of subjects (300 subjects as test subjects) and simultaneously exploit key point features for improving the matching accuracy. The experimental results are presented on publicly available database, the Ubripr database, and suggest effectiveness of RBM feature learning for automated periocular recognition with the large number of subjects. The results from the investigation in this paper also suggest that the supervised metric learning can be effectively used to achieve superior performance than the conventional Euclidean distance metric for the periocular identification.
Description: 22nd International Conference on Pattern Recognition, ICPR 2014, Sweden, 24-28 August 2014
URI: http://hdl.handle.net/10397/23101
ISBN: 978-1-4799-5209-0 (electronic)
978-1-4799-5210-6 (print on demand (PoD))
ISSN: 1051-4651
DOI: 10.1109/ICPR.2014.77
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

7
Last Week
1
Last month
1
Citations as of Sep 11, 2017

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
1
Citations as of Sep 24, 2017

Page view(s)

54
Last Week
0
Last month
Checked on Sep 24, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.