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Information theoretic combination of classifiers with application to face detection

Publications associées (148)

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Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...

2004

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...

2004

Real-Time Face Detection Using Boosting Learning in Hierarchical Feature Spaces

Daniel Gatica-Perez, Dong Zhang

Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the bo ...

2004

A Study of the Effects of Score Normalisation Prior to Fusion in Biometric Authentication Tasks

Samy Bengio, Norman Hoon Thian Poh

Although the subject of fusion is well studied, the effects of normalisation prior to fusion are somewhat less well investigated. In this study, four normalisation techniques and six commonly used fusion classifiers were examined. Based on 24 (fusion class ...

IDIAP2004

On the Use of Speech and Face Information for Identity Verification

{T}his report first provides a review of important concepts in the field of information fusion, followed by a review of important milestones in audio-visual person identification and verification. {S}everal recent adaptive and non-adaptive techniques for r ...

IDIAP2004

Face Class Modeling Using Mixture of SVMs

Jean-Philippe Thiran, Vlad Popovici, Julien Meynet

We present a method for face detection which uses a new SVM structure trained in an expert manner in the eigenface space. This robust method has been introduced as a post processing step in a realtime face detection system. The principle is to train severa ...

EPFL2004

Adaptive Kernel Matching Pursuit for Pattern Classification

Jean-Philippe Thiran, Vlad Popovici

A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the numb ...

IEEE2004

Boosting Pixel-based Classifiers for Face Verification

Sébastien Marcel, Yann Rodriguez

The performance of face verification systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machine ...

IDIAP2003

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

IDIAP2003

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

IDIAP2003