Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33190
Title: Gait flow image : a silhouette-based gait representation for human identification
Authors: Lam, THW
Cheung, KH
Liu, JNK
Keywords: Gait representation
Gait recognition
Gait flow image
Biometrics
Issue Date: 2011
Publisher: Elsevier
Source: Pattern recognition, 2011, v. 44, no. 4, p. 973-987 How to cite?
Journal: Pattern recognition 
Abstract: In this paper, we propose a novel gait representation—gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83%, which is better than GEI by 3.75%. In the dimensional reduction approach, GFI achieved an average identification rate 43.08%, which is better than GEI by 1.5%. The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.
URI: http://hdl.handle.net/10397/33190
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2010.10.011
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

123
Last Week
2
Last month
Citations as of Jul 29, 2018

WEB OF SCIENCETM
Citations

81
Last Week
1
Last month
1
Citations as of Aug 11, 2018

Page view(s)

119
Last Week
5
Last month
Citations as of Aug 13, 2018

Google ScholarTM

Check

Altmetric


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