AdaBoost Multiple Feature Selection and Combination for Face Recognition

Authors:
Francisco Martínez-Contreras;Carlos Orrite-Uruñuela;Jesús Martínez-Del-Rincón
Affiliations:
CVLab, Aragon Institute for Engineering Research, University of Zaragoza, Spain;CVLab, Aragon Institute for Engineering Research, University of Zaragoza, Spain;CVLab, Aragon Institute for Engineering Research, University of Zaragoza, Spain
Venue:
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Year:
2009

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Abstract

Gabor features have been recognized as one of the most successful face representations. Encouraged by the results given by this approach, other kind of facial representations based on Steerable Gaussian first order kernels and Harris corner detector are proposed in this paper. In order to reduce the high dimensional feature space, PCA and LDA techniques are employed. Once the features have been extracted, AdaBoost learning algorithm is used to select and combine the most representative features. The experimental results on XM2VTS database show an encouraging recognition rate, showing an important improvement with respect to face descriptors only based on Gabor filters.