Gait Recognition by Ranking
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http://dx.doi.org/10.1007/978-3-642-33718-5_24 |
Metadatos
Título
Gait Recognition by RankingFecha de publicación
2012Editor
Springer Berlin HeidelbergISBN
978-3-642-33717-8ISSN
0302-9743; 1611-3349Cita bibliográfica
Martín Félez, Raúl ; Xiang, Tao. " Gait Recognition by Ranking". En: Computer Vision-ECCV 2012 – 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I / Fitzgibbon, Andrew [et al.] (Eds.). Berlin : Springer, 2012. (Lecture Notes in Computer Science; 7572) . ISBN: 978-3-642-33717-8, pp. 328-341Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007%2F978-3-642-33718-5_24Palabras clave / Materias
Resumen
The advantage of gait over other biometrics such as face or fingerprint is that it can operate from a distance and without subject cooperation. However, this also makes gait subject to changes in various covariate ... [+]
The advantage of gait over other biometrics such as face or fingerprint is that it can operate from a distance and without subject cooperation. However, this also makes gait subject to changes in various covariate conditions including carrying, clothing, surface and view angle. Existing approaches attempt to address these condition changes by feature selection, feature transformation or discriminant subspace learning. However, they suffer from lack of training samples from each subject, can only cope with changes in a subset of conditions with limited success, and are based on the invalid assumption that the covariate conditions are known a priori. They are thus unable to perform gait recognition under a genuine uncooperative setting. We propose a novel approach which casts gait recognition as a bipartite ranking problem and leverages training samples from different classes/people and even from different datasets. This makes our approach suitable for recognition under a genuine uncooperative setting and robust against any covariate types, as demonstrated by our extensive experiments. [-]
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