Face recognition at a distance: Scenario analysis and applications
Metadatos
Title:
Face recognition at a distance: Scenario analysis and applications
Author:
Vera-Rodríguez, Rubén; Tomé González, Pedro; Ortega-García, Javier; Fiérrez, Julián
Entity:
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
UAM Author:
Fierrez Aguilar, Julián
; Tome González, Pedro
Publisher:
Springer Berlin Heidelberg
Date:
2010
Citation:
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:
1867-5662
ISBN:
978-3-642-14882-8 (print); 978-3-642-14883-5 (online)
DOI:
10.1007/978-3-642-14883-5_44
Funded by:
This work has been supported by project Contexts (S2009/TIC-1485).
Project:
Comunidad de Madrid. S2009/TIC-1485/CONTEXTS
Editor's Version:
http://dx.doi.org/10.1007/978-3-642-14883-5_44
Subjects:
Computational Intelligence; Artificial Intelligence; Robotics; Telecomunicaciones
Note:
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-14883-5_44
Proceedings of the International Symposium of Distributed Computing and Artificial Intelligence held in Valencia (Spain).
Rights:
© Springer-Verlag Berlin Heidelberg 2010
Abstract:
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.
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