Mixtures of Boosted Classifiers for Frontal Face Detection

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.


Published in:
Signal, Image and Video Processing, 1, 1, 29--38
Year:
2007
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 Record created 2006-10-27, last modified 2018-01-27

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