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Mixtures of Boosted Classifiers for Frontal Face Detection

Publications associées (56)

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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

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

Randomized Trees for Real-Time Keypoint Recognition

Pascal Fua, Vincent Lepetit, Pascal Lagger

In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our ...

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

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

Daniel Gatica-Perez, Dong Zhang

IDIAP2003

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 ...

2003

Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS

Sébastien Marcel

IDIAP2003

Face Verification Using Synthesized Non-Frontal Models

Samy Bengio

{NOTE}: {THIS} {REPORT} {HAS} {BEEN} {SUPERSEDED} {BY} {IDIAP-RR} 04-04. {I}n this report we address the problem of non-frontal face verification when only a frontal training image is available (e.g. a passport photograph) by augmenting a client's frontal ...

IDIAP2003

New Entropy Based Combination Rules in HMM/ANN Multi-stream ASR

Hervé Bourlard, Hemant Misra, Vivek Tyagi

Classifier performance is often enhanced through combining multiple streams of information. In the context of multi-stream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution outp ...

2003

Optimal Steerable Filters for Feature Detection

Michaël Unser, Mathews Jacob

We present a new approach for the design of optimal steerable 2-D templates for feature detection. As opposed to classical schemes where the optimal 1-D template is derived and extended to 2-D, we directly obtain the 2-D template. We choose the template fr ...

IEEE2003