Face recognition at a distance: Scenario analysis and applications
Entidad
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesEditor
Springer Berlin HeidelbergFecha de edición
2010Cita
10.1007/978-3-642-14883-5_44
Distributed Computing and Artificial Intelligence: 7th International Symposium. Advances in Intelligent and Soft Computing, Volumen 79. Springer, 2010. 341-348
ISSN
1867-5662ISBN
978-3-642-14882-8 (print); 978-3-642-14883-5 (online)DOI
10.1007/978-3-642-14883-5_44Financiado por
This work has been supported by project Contexts (S2009/TIC-1485).Proyecto
Comunidad de Madrid. S2009/TIC-1485/CONTEXTSVersión del editor
http://dx.doi.org/10.1007/978-3-642-14883-5_44Materias
Computational Intelligence; Artificial Intelligence; Robotics; TelecomunicacionesNota
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-14883-5_44Proceedings of the International Symposium of Distributed Computing and Artificial Intelligence held in Valencia (Spain).
Derechos
© Springer-Verlag Berlin Heidelberg 2010Resumen
Face recognition is the most popular biometric used in applications at a
distance, which range from high security scenarios such as border control to others
such as video games. This is a very challenging task since there are many varying
factors (illumination, pose, expression, etc.) This paper reports an experimental
analysis of three acquisition scenarios for face recognition at a distance, namely:
close, medium, and far distance between camera and query face, the three of them
considering templates enrolled in controlled conditions. These three representative
scenarios are studied using data from the NIST Multiple Biometric Grand Challenge,
as the first step in order to understand the main variability factors that affect
face recognition at a distance based on realistic yet workable and widely available
data. The scenario analysis is conducted quantitatively in two ways. First, an analysis
of the information content in segmented faces in the different scenarios. Second,
an analysis of the performance across scenarios of three matchers, one commercial,
and two other standard approaches using popular features (PCA and DCT) and
matchers (SVM and GMM). The results show to what extent the acquisition setup
impacts on the verification performance of face recognition at a distance.
Lista de ficheros
Google Scholar:Vera Rodríguez, Rubén
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Tomé González, Pedro
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Ortega García, Javier
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Fiérrez Aguilar, Julián
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