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Document type:
Zeitschriftenaufsatz 
Author(s):
Babaee, Ma.; Li, L.; Rigoll, G. 
Title:
Person identification from partial gait cycle using fully convolutional neural networks 
Abstract:
Gait as a biometric property for person identification plays a key role in video surveillance and security applications. In gait recognition, normally, gait feature such as Gait Energy Image (GEI) is extracted from one full gait cycle. However in many circumstances, such a full gait cycle might not be available due to occlusion. Thus, the GEI is not complete giving rise to a degrading in gait-based person identification rate. In this paper, we address this issue by proposing a novel method to id...    »
 
Journal title:
Neurocomputing 
Year:
2019 
Month:
Apr 
Journal issue:
Vol. 338 
Pages contribution:
pp. 116-125 
Publisher:
Elsevier 
TUM Institution:
Lehrstuhl für Mensch-Maschine-Kommunikation