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David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2002). Motion-induced error correction in ultrasound imaging..
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David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to MR Volume Registration for Brain Sinking Measurement.
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David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to magnetic resonance volume registration for brain sinking measurement.
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David Masip. (2003). Dimensionality reduction techniques applied to nearest neighbor classification.
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David Masip. (2005). Face Classification Using Discriminative Features and Classifier Combination (Jordi Vitria, Ed.). Ph.D. thesis, , .
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David Masip, Agata Lapedriza, & Jordi Vitria. (2007). Measuring External Face Appearance for Face Classification. In Face Recognition, Ed. Kresimir Delac and Mislav Grgic, pp. 287–307, ISBN 978–3–902613–03–5, I–Tech Education and Publishing.
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David Masip, Agata Lapedriza, & Jordi Vitria. (2008). Multitask Learning: An Application to Incremental Face Recognition. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 1, 585–590).
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David Masip, Agata Lapedriza, & Jordi Vitria. (2007). Face Verification Sharing Knowledge from Different Subjects. In 2nd International Conference on Computer Vision Theory and Applications (Vol. 2, 268–289).
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David Masip, Agata Lapedriza, & Jordi Vitria. (2009). Boosted Online Learning for Face Recognition. TSMCB - IEEE Transactions on Systems, Man and Cybernetics part B, 39(2), 530–538.
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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David Masip, Alexander Todorov, & Jordi Vitria. (2012). The Role of Facial Regions in Evaluating Social Dime. In Rita Cucchiara V. M. Andrea Fusiello (Ed.), 12th European Conference on Computer Vision – Workshops and Demonstrations (Vol. 7584, pp. 210–219). LNCS. Springer Berlin Heidelberg.
Abstract: Facial trait judgments are an important information cue for people. Recent works in the Psychology field have stated the basis of face evaluation, defining a set of traits that we evaluate from faces (e.g. dominance, trustworthiness, aggressiveness, attractiveness, threatening or intelligence among others). We rapidly infer information from others faces, usually after a short period of time (< 1000ms) we perceive a certain degree of dominance or trustworthiness of another person from the face. Although these perceptions are not necessarily accurate, they influence many important social outcomes (such as the results of the elections or the court decisions). This topic has also attracted the attention of Computer Vision scientists, and recently a computational model to automatically predict trait evaluations from faces has been proposed. These systems try to mimic the human perception by means of applying machine learning classifiers to a set of labeled data. In this paper we perform an experimental study on the specific facial features that trigger the social inferences. Using previous results from the literature, we propose to use simple similarity maps to evaluate which regions of the face influence the most the trait inferences. The correlation analysis is performed using only appearance, and the results from the experiments suggest that each trait is correlated with specific facial characteristics.
Keywords: Workshops and Demonstrations
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David Masip, & Jordi Vitria. (2003). An Experimental Comparision of Dimensionality Reduction for Face Verification Methods.
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David Masip, & Jordi Vitria. (2003). On the Nearest Neighbor Approach for Gender Recognition.
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David Masip, & Jordi Vitria. (2003). On the Nearest Neighbor Approach for Gender Recognition.
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David Masip, & Jordi Vitria. (2004). Real Time Face Detection and Verification for Uncontrolled Environments.
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David Masip, & Jordi Vitria. (2004). Object Recognition using Boosted Adaptive Features..
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