Analysis of gait recognition on constrained scenarios with limited data information
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Springer Berlin HeidelbergDate
2013-08Citation
10.1007/978-3-642-38061-7_23
Highlights on Practical Applications of Agents and Multi-Agent Systems: International Workshops of PAAMS. Communications in Computer and Information Science, Volumen 365. Springer, 2013. 231-239
ISSN
1865-0929ISBN
978-3-642-38060-0 (print); 978-3-642-38061-7 (online)DOI
10.1007/978-3-642-38061-7_23Funded by
This work has been supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186), Bio-Shield (TEC2012-34881) and "Cátedra UAM-Telefonica". Rubén Vera-Rodríguez is supported by a Juan de la Cierva Fellowship from the Spanish MINECO.Project
Comunidad de Madrid. S2009/TIC-1485/CONTEXTSEditor's Version
http://dx.doi.org/10.1007/978-3-642-38061-7_23Subjects
Biometrics; Gait recognition; Video surveillance; TelecomunicacionesNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38061-7_23Proceedings of International Workshops of Practical Applications of Agents and Multi-Agent Systems (PAAMS), held in 2013, Salamanca (Spain).
Rights
© Springer-Verlag Berlin Heidelberg 2013Abstract
This paper is focused on the assessment of gait recognition on a constrained scenario, where limited information can be extracted from the gait image sequences. In particular we are interested in assessing the performance of gait images when only the lower part of the body is acquired by the camera and just half of a gait cycle is available (SFootBD database). Thus, various state-of-the-art feature approaches have been followed and applied to the data. Results show that good recognition performance can be achieved using such limited data information for gait biometric. A comparative analysis of the influence of the quantity of data used in the training models has been carried out obtaining results of 8.6% EER for the case of using 10 data samples to train the models, and 5.7% of EER for the case of using 40 data for training. Also, a comparison with a standard and ideal gait database (USF database) is also carried out using similar experimental protocols. In this case 10 data samples are used for training achieving results of 3.6% EER. The comparison with a standard database shows that different feature approaches perform differently for each database, achieving best individual results with MPCA and EGEI methods for the SFootBD and the USF databases respectively
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Google Scholar:Vera Rodríguez, Rubén
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Gabriel Sanz, Silvia
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Fiérrez Aguilar, Julián
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Tomé González, Pedro
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Ortega García, Javier
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