Publication

Information theoretic combination of classifiers with application to face detection

Related publications (148)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...

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

Independent Component Analysis and Support Vector Machine for Face Feature Extraction

Jean-Philippe Thiran, Gianluca Antonini, Vlad Popovici

We propose Independent Component Analysis representation and Support Vector Machine classification to extract facial features in a face detection/localization context. The goal is to find a better space where project the data in order to build ten differen ...

IEEE2003

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 ...

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

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

Online Policy Adaptation for Ensemble Algorithms

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms are general methods for improving the performance of a given learning algorithm. This is achieved by the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and com ...

IDIAP2002

An Improved Predictive Accuracy Bound for Averaging Classifiers

Matthias Seeger

We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popular averaging techniques such as Bayesian classification, Maximum Entropy dis ...

2001

Evaluation of SVM Binary Classification with Nonparametric Stochastic Simulations

The quality of Support Vector Machines binary classification of spatial environmental data is evaluated with geostatistical nonparametrtic conditional stochastic simulations. Equally probable realizations are generated and compared with SVM. Case study is ...

idiap2001