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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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Abstract |
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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Author |
Petia Radeva; Jordi Vitria |
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Corkinspect: Statistical Learning of Natural Material |
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2004 |
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Italian Beverage Technology, 13(38):11–18 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RaV2004b |
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514 |
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Bogdan Raducanu; Jordi Vitria |
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Title |
Face Recognition by Artificial Vision Systems: A Cognitive Perspective |
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2008 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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22 |
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5 |
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899–913 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008b |
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1007 |
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Author |
Santiago Segui; Laura Igual; Jordi Vitria |
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Title |
Bagged One Class Classifiers in the Presence of Outliers |
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2013 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
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5 |
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1350014-1350035 |
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One-class Classifier; Ensemble Methods; Bagging and Outliers |
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The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way. |
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OR; 600.046;MV |
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Admin @ si @ SIV2013 |
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2256 |
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David Masip; Jordi Vitria |
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Title |
Feature Extraction for Nearest Neighbor Classification. Application to Gender Recognition |
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2005 |
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International Journal of Intelligent Systems, 20(5): 561–576 (IF: 0.657) |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2005 |
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562 |
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