Performance characterisation of face recognition algorithms and their sensitivity to severe illumination changes

Authors:
Kieron Messer;Josef Kittler;James Short;G. Heusch;Fabien Cardinaux;Sebastien Marcel;Yann Rodriguez;Shiguang Shan;Y. Su;Wen Gao;X. Chen
Affiliations:
University of Surrey, Guildford, Surrey, UK;University of Surrey, Guildford, Surrey, UK;University of Surrey, Guildford, Surrey, UK;Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny, Switzerland;Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny, Switzerland;Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny, Switzerland;Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny, Switzerland;Institute of Computing Technology, Chinese Academy of Sciences, China;Institute of Computing Technology, Chinese Academy of Sciences, China;Institute of Computing Technology, Chinese Academy of Sciences, China;Institute of Computing Technology, Chinese Academy of Sciences, China
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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Abstract

This paper details the results of a face verification competition [2] held in conjunction with the Second International Conference on Biometric Authentication. The contest was held on the publically available XM2VTS database [4] according to a defined protocol [15]. The aim of the competition was to assess the advances made in face recognition since 2003 and to measure the sensitivity of the tested algorithms to severe changes in illumination conditions. In total, more than 10 algorithms submitted by three groups were compared. The results show that the relative performance of some algorithms is dependent on training conditions (data, protocol) as well as environmental changes.