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Multivariate Boosting with Look-Up Tables for Face Processing

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Prior knowledge in Kernel methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

EPFL2006

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

Hierarchical Indexing using R-trees for Replica Detection

Touradj Ebrahimi

Replica detection is a prerequisite for the discovery of copyright infringement and detection of illicit content. For this purpose, content-based systems can be an efficient alternative to watermarking. Rather than imperceptibly embedding a signal, content ...

2006

Feature Harvesting for Tracking-by-Detection

Pascal Fua, François Fleuret, Vincent Lepetit, Mustafa Özuysal

We propose a fast approach to 3D object detection and pose estimation that owes its robustness to a training phase during which the target object slowly moves with respect to the camera. No additional information is provided to the system, save a very roug ...

Springer2006

A Novel Replica Detection System using Binary Classifiers, R-trees, and PCA

Touradj Ebrahimi

Replica detection is a prerequisite for the discovery of copyright infringement and detection of illicit content. For this purpose, contentbased systems can be an efficient alternative to watermarking. Rather than imperceptibly embedding a signal, content- ...

IEEE2006

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

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

Kernel-based classifiers with applications to face detection

Vlad Popovici

Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...

EPFL2004

Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation

Pascal Fua, Vincent Lepetit, Julien Pilet

We propose a novel approach to point matching under large viewpoint and illumination changes that is suitable for accurate object pose estimation at a much lower computational cost than state-of-the-art methods. Most of these methods rely either on using a ...

2004

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