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Title:

Person identification from partial gait cycle using fully convolutional neural networks

Document type:
Zeitschriftenaufsatz
Author(s):
Babaee, Ma.; Li, L.; Rigoll, G.
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
Fulltext / DOI:
doi:10.1016/j.neucom.2019.01.091
Publisher:
Elsevier
TUM Institution:
Lehrstuhl für Mensch-Maschine-Kommunikation
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