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Mixtures of boosted classifiers for frontal face detection

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Abstract

This paper describes a new approach to automatic frontal face detection which employs Gaussian filters as local image descriptors. We then show how the paradigm of classifier combination can be used for building a face detector that outperforms the current state-of-the-art systems, while remaining fast enough for being used in real–time systems. It is based on the combination of several parallel classifiers trained on subsets of the complete training set. We report a number of results on some reference datasets and we use an unbiased method for comparing the detectors.

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Authors and Affiliations

  1. Ecole Polytechnique Fédérale de Lausanne (EPFL), Signal Processing Institute, CH-1015, Lausanne, Switzerland

    Julien Meynet & Jean-Philippe Thiran

  2. Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland

    Vlad Popovici

Authors
  1. Julien Meynet
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  2. Vlad Popovici
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  3. Jean-Philippe Thiran
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Correspondence to Julien Meynet.

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Meynet, J., Popovici, V. & Thiran, JP. Mixtures of boosted classifiers for frontal face detection. SIViP 1, 29–38 (2007). https://doi.org/10.1007/s11760-007-0003-x

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  • DOI: https://doi.org/10.1007/s11760-007-0003-x

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