Ponencia
Persistent homology-based gait recognition robust to upper body variations
Autor/es | Lamar León, Javier
Alonso Baryolo, Raúl García Reyes, Edel González Díaz, Rocío ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
Departamento | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) |
Fecha de publicación | 2016 |
Fecha de depósito | 2021-10-15 |
Publicado en |
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ISBN/ISSN | 978-1-5090-4847-2 |
Resumen | Gait recognition is nowadays an important biometric
technique for video surveillance tasks, due to the advantage of
using it at distance. However, when the upper body movements
are unrelated to the natural dynamic of ... Gait recognition is nowadays an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. However, when the upper body movements are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat, the reported results show low accuracy. With the goal of solving this problem, we apply persistent homology to extract topological features from the lowest fourth part of the body silhouettes. To obtain the features, we modify our previous algorithm for gait recognition, to improve its efficacy and robustness to variations in the amount of simplices of the gait complex. We evaluate our approach using the CASIA-B dataset, obtaining a considerable accuracy improvement of 93:8%, achieving at the same time invariance to upper body movements unrelated with the dynamic of the gait. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | MTM2015-67072-P
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Cita | Lamar León, J., Alonso Baryolo, R., García Reyes, E. y González Díaz, R. (2016). Persistent homology-based gait recognition robust to upper body variations. En ICPR 2016: 23rd International Conference on Pattern Recognition (1083-1088), Cancún, México: IEEE Computer Society. |
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