Publication

Mixtures of Boosted Classifiers for Frontal Face Detection

Jean-Philippe Thiran, Vlad Popovici, Julien Meynet
2007
Journal Articles

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.

Official source

https://infoscience.epfl.ch/entities/publication/d51736be-6110-4d9f-a594-3ba0ad1689ed

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Jean-Philippe Thiran, Vlad Popovici, Julien Meynet
2007
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/d51736be-6110-4d9f-a594-3ba0ad1689ed

About this result

Ontological neighbourhood

Information engineering

Artificial intelligence: Computer vision

Related concepts (26)

Boosting (machine learning)

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).

Ensemble learning

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

Naive Bayes classifier

In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.

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