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David Masip; Agata Lapedriza; Jordi Vitria |
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Title |
Boosted Online Learning for Face Recognition |
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Journal Article |
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Year |
2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
Issue |
2 |
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530–538 |
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Abstract  |
Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
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1083–4419 |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ MLV2009 |
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1155 |
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Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
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Title |
Minimal Design of Error-Correcting Output Codes |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition Letters |
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PRL |
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33 |
Issue |
6 |
Pages |
693-702 |
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Keywords |
Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
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Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Elsevier |
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0167-8655 |
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MILAB; OR;HuPBA;MV |
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Admin @ si @ BEB2011a |
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1800 |
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Author |
Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol |
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Title |
Online Error-Correcting Output Codes |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition Letters |
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PRL |
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32 |
Issue |
3 |
Pages |
458-467 |
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IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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Elsevier |
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North Holland |
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0167-8655 |
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MILAB;OR;HuPBA;MV |
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Admin @ si @ EMP2011 |
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1714 |
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Author |
A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |
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Title |
Topological principal component analysis for face encoding and recognition |
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Year |
2001 |
Publication |
Pattern Recognition Letters |
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PRL |
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22 |
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6-7 |
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769–776 |
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Abstract |
IF: 0.552 |
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ADAS;OR;MV |
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ADAS @ adas @ PVL2001 |
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155 |
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Author |
M. Bressan; Jordi Vitria |
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Title |
Nonparametric Discriminant Analysis and Nearest Neighbor Classification |
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Year |
2003 |
Publication |
Pattern Recognition Letters |
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PRL |
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24 |
Issue |
15 |
Pages |
2743–2749 |
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Abstract |
IF: 0.809 |
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OR;MV |
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BCNPCL @ bcnpcl @ BrV2003b |
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367 |
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