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Face Class Modeling Using Mixture of SVMs

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Face Detection with Mixtures of Boosted Discriminant Features

Jean-Philippe Thiran, Vlad Popovici, Julien Meynet

Detecting faces in images is a key step in numerous computer vision applications as face recognition for example. Face detection is a difficult task in image analysis because of the large face intra-class variability which is due to the important influence ...

2005

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

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

Invariances in Kernel Methods: From Samples to Objects

Samy Bengio, Alexei Pozdnoukhov

This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assumi ...

IDIAP2004

Face Detection using SVM Trained in Eigenfaces Space

Jean-Philippe Thiran, Vlad Popovici

The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of ei ...

SPIE2003

Face Class Modeling in Eigenfaces Space

Jean-Philippe Thiran, Vlad Popovici

We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, genera ...

SPIE2003

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

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...

EPFL2003

Fast Circle Detection Using Gradient Pair Vectors

Ali Ajdari Rad

The Circle Hough Transform (CHT) has become a common method for circle detection in numerous image processing applications. Because of its drawbacks, various modifications to the basic CHT method have been suggested. This paper presents an algorithm to fin ...

CSIRO Publishing2003

A new method for manufacturing nanostructured electrodes on glass substrates

Ulf Anders Hagfeldt

The present paper describes a new method for manufg. a nanostructured porous layer of TiO2 on a conducting glass substrate for use in a dye-sensitized photoelectrochem. cell. The method involves deposition of a layer of semiconductor particles onto a condu ...

2002