Uncooperative gait recognition by learning to rank
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http://dx.doi.org/10.1016/j.patcog.2014.06.010 |
Metadatos
Título
Uncooperative gait recognition by learning to rankFecha de publicación
2014Editor
ElsevierISSN
0031-3203Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0031320314002325Palabras clave / Materias
Resumen
Gait is a useful biometric because it can operate from a distance and without subject cooperation.
However, it is affected by changes in covariate conditions (carrying, clothing, view angle, etc.). Existing
methods ... [+]
Gait is a useful biometric because it can operate from a distance and without subject cooperation.
However, it is affected by changes in covariate conditions (carrying, clothing, view angle, etc.). Existing
methods suffer from lack of training samples, can only cope with changes in a subset of conditions with
limited success, and implicitly assume subject cooperation. We propose a novel approach which casts
gait recognition as a bipartite ranking problem and leverages training samples from different people and
even from different datasets. By exploiting learning to rank, the problem of model over-fitting caused by
under-sampled training data is effectively addressed. This makes our approach suitable under a genuine
uncooperative setting and robust against changes in any covariate conditions. Extensive experiments
demonstrate that our approach drastically outperforms existing methods, achieving up to 14-fold
increase in recognition rate under the most difficult uncooperative settings. [-]
Publicado en
Pattern Recognition 47 (2014) 3793–3806Derechos de acceso
© 2014 Elsevier Ltd. All rights reserved.
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