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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/21292

Title: Gait recognition for human identification using ensemble of LVQ Neural Networks
Authors: Kordjazi, Neda
Rahati, Saeid
???metadata.dc.contributor.url???: Neda.kordjazi@gmail.com
Keywords: Gait recognition;Learning Vector Quantization (LVQ);Neural network ensemble;Majority voting fusion method;Principal Component Analysis (PCA)
Issue Date: 27-Feb-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 180-185
Series/Report no.: Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012)
Abstract: 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.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179001
http://hdl.handle.net/123456789/21292
ISBN: 978-145771989-9
Appears in Collections:Conference Papers

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