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

Publications associées (35)

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Information theoretic combination of classifiers with application to face detection

Julien Meynet

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...

EPFL2007

Ensembles for sequence learning

Christos Dimitrakakis

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

EPFL2007

Fast Multiview Face Tracking with Pose Estimation

Jean-Philippe Thiran, Javier Cruz Mota, Julien Meynet

In this paper, a fast and effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier ext ...

2007

Ensembles for Sequence Learning

Christos Dimitrakakis

École Polytechnique Fédérale de Lausanne2006

Face Pose Estimation using a Tree of Boosted Classifiers

Jean-Philippe Thiran, Javier Cruz Mota, Julien Meynet

Face detection in images or video sequences is a very challenging problem. It has a wide range of applications but at the same time it presents a great number of difficulties, since faces are non-rigid and very changeable objects that can adopt a lot of di ...

2006

A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

Touradj Ebrahimi, Ulrich Hoffmann

Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) tri ...

SPIE2005

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

Boosting Pixel-based Classifiers for Face Verification

Sébastien Marcel, Yann Rodriguez

The performance of face authentication 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 Machi ...

Springer-Verlag2004

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

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

2004