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Comparison of the nearest feature classifiers for face recognition

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Abstract

This paper presents an experimental comparison of the nearest feature classifiers, using an approach based on binomial tests in order to evaluate their strengths and weaknesses. In addition, classification accuracies and the accuracy-dimensionality tradeoff have been considered as comparison criteria. We extend two of the nearest feature classifiers to label the query point by a majority vote of the samples. Comparisons were carried out for face recognition using ORL database. We apply the eigenface representation for feature extraction. Experimental results showed that even though the classification accuracy of k-NFP outperforms k-NFL in some dimensions, these rate differences do not have statistical significance.

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Authors and Affiliations

  1. Grupo de Control y Procesamiento Digital de Señales, Universidad Nacional de Colombia Sede Manizales, Carrera 27 # 64-60, Manizales (Caldas), Colombia

    Mauricio Orozco-Alzate & César Germán Castellanos-Domínguez

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  1. Mauricio Orozco-Alzate
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  2. César Germán Castellanos-Domínguez
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Correspondence to César Germán Castellanos-Domínguez.

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Orozco-Alzate, M., Castellanos-Domínguez, C.G. Comparison of the nearest feature classifiers for face recognition. Machine Vision and Applications 17, 279–285 (2006). https://doi.org/10.1007/s00138-006-0037-z

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  • DOI: https://doi.org/10.1007/s00138-006-0037-z

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