Face Recognition Using Angular LDA and SVM Ensembles

Authors:
R. S. Smith;J. Kittler;M. Hamouz;J. Illingworth
Affiliations:
University of Surrey, Guildford, Surrey, GU2 7XH, UK;University of Surrey, Guildford, Surrey, GU2 7XH, UK;University of Surrey, Guildford, Surrey, GU2 7XH, UK;University of Surrey, Guildford, Surrey, GU2 7XH, UK
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 2

Discriminant analysis in correlation similarity measure space

Proceedings of the 24th international conference on Machine learning
A method for estimating authentication performance over time, with applications to face biometrics

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

One successful approach to feature extraction in face recognition problems is that of linear discriminant analysis (LDA). We examine an extension of this technique, called angular LDA, in which a non-linear transformation is applied after the LDA representation has been determined. We present experimental evidence, using the XM2VTS face database, that an ensemble of SVM classifiers operating in the angular LDA space is capable of making more accurate face verification and identification decisions than the same classifiers operating in the standard LDA space. We also compare experimentally the relative effectiveness of a number of techniques for ensemble design, ensemble decoding metric and SVM calibration algorithm.