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M. Bressan; Jordi Vitria |
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Nonparametric Discriminant Analysis and Nearest Neighbor Classification |
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2003 |
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Pattern Recognition Letters |
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24 |
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15 |
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2743–2749 |
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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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Author |
Agata Lapedriza; David Masip; Jordi Vitria |
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Title |
On the Use of External Face Features for Identity Verification |
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2006 |
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Journal of Multimedia, 1(4): 11–20 |
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1 |
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4 |
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11-20 |
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Face Verification, Computer Vision, Machine Learning |
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In general automatic face classification applications images are captured in natural environments. In these cases, the performance is affected by variations in facial images related to illumination, pose, occlusion or expressions. Most of the existing face classification systems use only the internal features information, composed by eyes, nose and mouth, since they are more difficult to imitate. Nevertheless, nowadays a lot of applications not related to security are developed, and in these cases the information located at head, chin or ears zones (external features) can be useful to improve the current accuracies. However, the lack of a natural alignment in these areas makes difficult to extract these features applying classic Bottom-Up methods. In this paper, we propose a complete scheme based on a Top-Down reconstruction algorithm to extract external features of face images. To test our system we have performed face verification experiments using public databases, given that identity verification is a general task that has many real life applications. We have considered images uniformly illuminated, images with occlusions and images with high local changes in the illumination, and the obtained results show that the information contributed by the external features can be useful for verification purposes, specially significant when faces are partially occluded. |
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BCNPCL @ bcnpcl @ LMV2006b |
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708 |
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Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol |
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Title |
Online Error-Correcting Output Codes |
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2011 |
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Pattern Recognition Letters |
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PRL |
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32 |
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3 |
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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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Bogdan Raducanu; Jordi Vitria |
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Title |
Online Nonparametric Discriminant Analysis for Incremental Subspace Learning and Recognition |
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2008 |
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Pattern Analysis and Applications. Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications |
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11 |
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3-4 |
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259–268 |
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Springer |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008c |
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997 |
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Bogdan Raducanu; Jordi Vitria; Ales Leonardis |
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Online pattern recognition and machine learning techniques for computer-vision: Theory and applications |
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2010 |
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Image and Vision Computing |
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IMAVIS |
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28 |
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7 |
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1063–1064 |
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(Editorial for the Special Issue on Online pattern recognition and machine learning techniques)
In real life, visual learning is supposed to be a continuous process. This paradigm has found its way also in artificial vision systems. There is an increasing trend in pattern recognition represented by online learning approaches, which aims at continuously updating the data representation when new information arrives. Starting with a minimal dataset, the initial knowledge is expanded by incorporating incoming instances, which may have not been previously available or foreseen at the system’s design stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest in this subject, the aim of this special issue is to be a landmark event in the development of online learning techniques and their applications with the hope that it will capture the interest of a wider audience and will attract even more researchers. We received 19 contributions, of which 9 have been accepted for publication, after having been subjected to usual peer review process. |
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Elsevier |
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0262-8856 |
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OR;MV |
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BCNPCL @ bcnpcl @ RVL2010 |
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1280 |
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