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Julien Meynet

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Publications associées (16)

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Information theoretic combination of pattern classifiers

Jean-Philippe Thiran, Julien Meynet

Combining several classifiers has proved to be an effective machine learning technique. Two concepts clearly influence the performances of an ensemble of classifiers: the diversity between classifiers and the individual accuracies of the classifiers. In th ...

Elsevier2010

Ensembles of SVMs using an Information Theoretic Criterion

Jean-Philippe Thiran, Julien Meynet

Training Support Vector Machine can become very challenging in large scale problems. Training several lower complexity SVMs on local subsets of the training set can significantly reduce the training complexity and also improve the classification performanc ...

2008

Fast multi-view face tracking with pose estimation

Jean-Philippe Thiran, Javier Cruz Mota, Julien Meynet

In this paper, a fast and an 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 extra ...

2008

Fast multi-view 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 ex ...

2008

Analysis of Head Mounted Wireless Camera Videos for Early Diagnosis of Autism

Jean-Philippe Thiran, Aude Billard, Julien Meynet, Basilio Noris

In this paper we present a computer based approach to analysis of social interaction experiments for the diagnosis of autism spectrum disorders in young children of 6-18 months of age. We apply face detection on videos from a head-mounted wireless camera t ...

Springer-Verlag2007

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

Face Detection with Boosted Gaussian Features

Jean-Philippe Thiran, Vlad Popovici, Julien Meynet

Detecting faces in images is a key step in numerous computer vision applications, such as face recognition or facial expression analysis. Automatic face detection is a difficult task because of the large face intra-class variability which is due to the imp ...

2007

Mixtures of Boosted Classifiers for Frontal Face Detection

Jean-Philippe Thiran, Vlad Popovici, Julien Meynet

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 ...

2007

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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

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In this paper we propose a new approach to automatic human face detection which employs anisotropic Gaussian filters as local image descriptors. We then show how the paradigm of classifier combination can be used for building a face detector that outperfor ...

IEEE2006