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Titel:

Person identification from partial gait cycle using fully convolutional neural networks

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Zeitschriftentitel:
Neurocomputing
Jahr:
2019
Monat:
Apr
Heft / Issue:
Vol. 338
Seitenangaben Beitrag:
pp. 116-125
Volltext / DOI:
doi:10.1016/j.neucom.2019.01.091
Verlag / Institution:
Elsevier
TUM Einrichtung:
Lehrstuhl für Mensch-Maschine-Kommunikation
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