Assessment of gait recognition based on the lower part of the human body
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
IEEEDate
2013-08-15Citation
10.1109/IWBF.2013.6547321
2013 IEEE International Workshop on Biometrics and Forensics (IWBF). IEEE, 2013
ISBN
978-1-4673-4987-1DOI
10.1109/IWBF.2013.6547321Funded by
This work has been partially supported by a contract with Spanish Guardia Civil and projects Bio-Challenge (TEC2009-11186), Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and “Cátedra UAM-Telefónica”.Project
Comunidad de Madrid. S2009/TIC-1485/CONTEXTSEditor's Version
http://dx.doi.org/10.1109/IWBF.2013.6547321Subjects
Biometrics; Gait recognition; Surveillance; TelecomunicacionesNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. S. Gabriel-Sanz, R. Vera-Rodriguez, P. Tomé, J. Fiérrez, "Assessment of gait recognition based on the lower part of the human body" in International Workshop on Biometrics and Forensics (IWBF), Lisbon (Portugal), 2013, 1-4.Rights
© 2013 IEEEAbstract
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. A comparison with a standard and ideal gait database (USF database) is also carried out using similar experimental protocols. Results show that good recognition performance can be achieved using such limited data information for gait biometric (around 85% of rank 5 identification rate and 8.6% of 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 database respectively.
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Google Scholar:Gabriel Sanz, Silvia
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Vera Rodríguez, Rubén
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Tomé González, Pedro
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Fiérrez Aguilar, Julián
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