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Aggregation of classifiers based on image transformations in biometric face recognition

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Abstract

In this study, we investigate the use of collective knowledge of independent classifiers (experts) in the area of face recognition. We formulate a hypothesis and provide compelling experimental evidence behind it that different image transformations can offer unique discriminatory information useful for face classification. We show that such discriminatory information can be combined in order to increase classification rates over those being produced by individual classifiers. In particular, we focus on contrast enhancement realized by histogram equalization and edge detection carried out with the use of the Sobel operator. We construct feature spaces emerging from linear and nonlinear methods of dimensionality reduction, namely Eigenfaces, Fisherfaces, kernel-PCA, and Isomap. Aggregation of classifiers is accomplished by majority voting and a Bayesian product rule. Extensive experimentation is conducted using the well-known FERET and YALE datasets.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, 9107–116 Street, Edmonton, AB, Canada

    Gabriel Jarillo, Witold Pedrycz & Marek Reformat

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  1. Gabriel Jarillo
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  2. Witold Pedrycz
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  3. Marek Reformat
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Correspondence to Gabriel Jarillo.

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Jarillo, G., Pedrycz, W. & Reformat, M. Aggregation of classifiers based on image transformations in biometric face recognition. Machine Vision and Applications 19, 125–140 (2008). https://doi.org/10.1007/s00138-007-0088-9

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  • DOI: https://doi.org/10.1007/s00138-007-0088-9

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