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Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS

Publications associées (34)

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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

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

User Authentication via Adapted Statistical Models of Face Images

Samy Bengio

It has been previously demonstrated that systems based on local features and relatively complex statistical models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition. Recently, a simpler statistical model, namely t ...

IDIAP2004

Face Authentication using Client-specific Matching Pursuit

Jean-Philippe Thiran, Pierre Vandergheynst, Sébastien Marcel, Philippe Jost

In this paper, we address the problem of finding image decompositions that allow good compression performance, and that are also efficient for face authentication. We propose to decompose the face image using Matching Pursuit and to perform the face authen ...

IDIAP2004

Estimating the Quality of Face Localization for Face Verification

Samy Bengio, Yann Rodriguez

Face localization is the process of finding the exact position of a face in a given image. This can be useful in several applications such as face tracking or person authentication. The purpose of this paper is to show that the error made during the locali ...

IDIAP2004

Estimating the Quality of Face Localization for Face Verification

Samy Bengio, Yann Rodriguez

2004

On Performance / Robustness / Complexity Trade-Offs in Face Verification

Samy Bengio

In much of the literature devoted to face recognition, experiments are performed with controlled images (e.g. manual face localization, controlled lighting, background and pose); however, a practical recognition system has to be robust to more challenging ...

IDIAP2004

Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS

Sébastien Marcel

We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs), for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability ...

IDIAP2003

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