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Gait recognition for human identification using ensemble of LVQ Neural Networks

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Main Authors: Kordjazi, Neda, Rahati, Saeid
Other Authors: Neda.kordjazi@gmail.com
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Gait recognition
Learning Vector Quantization (LVQ)
Neural network ensemble
Majority voting fusion method
Principal Component Analysis (PCA)
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21292
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id my.unimap-21292
record_format dspace
spelling my.unimap-212922012-10-10T09:06:56Z Gait recognition for human identification using ensemble of LVQ Neural Networks Kordjazi, Neda Rahati, Saeid Neda.kordjazi@gmail.com Gait recognition Learning Vector Quantization (LVQ) Neural network ensemble Majority voting fusion method Principal Component Analysis (PCA) Link to publisher's homepage at http://ieeexplore.ieee.org/ Usage of gait biometric in individual identification is a rather new and encouraging research area in biometrics. Requiring no cooperation from the observed individual, and functionality from distance, using non-expensive low resolution cameras, are the benefits that have been dragging enormous attention to gait biometric. However, it should be noted that, gait pattern in humans can be greatly affected by changing of clothes, shoes, or even emotional states. This natural variability, which is absent in other biometrics being used for identification, such as fingerprint and iris, decreases the reliability of recognition. In this paper, a mixture of experts, in form of an LVQNN ensemble was employed to improve recognition rate and accuracy. Majority voting fusion method was used to combine the results of LVQNNs. First, local motion silhouette images (LMSIs) were generated from silhouette walking frame sequences. Then using PCA, lower dimensional features were extracted from LMSIs, and were fed to classifiers as inputs. Experiments were carried out using the silhouette dataset A of CASIA gait database, and the effectiveness of the proposed method is demonstrated. 2012-10-10T09:06:56Z 2012-10-10T09:06:56Z 2012-02-27 Working Paper p. 180-185 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179001 http://hdl.handle.net/123456789/21292 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Gait recognition
Learning Vector Quantization (LVQ)
Neural network ensemble
Majority voting fusion method
Principal Component Analysis (PCA)
spellingShingle Gait recognition
Learning Vector Quantization (LVQ)
Neural network ensemble
Majority voting fusion method
Principal Component Analysis (PCA)
Kordjazi, Neda
Rahati, Saeid
Gait recognition for human identification using ensemble of LVQ Neural Networks
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 Neda.kordjazi@gmail.com
author_facet Neda.kordjazi@gmail.com
Kordjazi, Neda
Rahati, Saeid
format Working Paper
author Kordjazi, Neda
Rahati, Saeid
author_sort Kordjazi, Neda
title Gait recognition for human identification using ensemble of LVQ Neural Networks
title_short Gait recognition for human identification using ensemble of LVQ Neural Networks
title_full Gait recognition for human identification using ensemble of LVQ Neural Networks
title_fullStr Gait recognition for human identification using ensemble of LVQ Neural Networks
title_full_unstemmed Gait recognition for human identification using ensemble of LVQ Neural Networks
title_sort gait recognition for human identification using ensemble of lvq neural networks
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21292
_version_ 1643793337326501888
score 13.222552

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