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David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
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Title |
Automated Prediction of Preferences Using Facial Expressions |
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Journal Article |
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Year |
2014 |
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PloS one |
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Plos |
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9 |
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2 |
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e87434 |
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We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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no |
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Admin @ si @ MNT2014 |
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2453 |
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Author |
Manuel Graña; Bogdan Raducanu |
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Special Issue on Bioinspired and knowledge based techniques and applications |
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Journal Article |
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2015 |
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Neurocomputing |
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NEUCOM |
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1-3 |
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no |
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Admin @ si @ GrR2015 |
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2598 |
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Author |
R. Clariso; David Masip; A. Rius |
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Student projects empowering mobile learning in higher education |
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2014 |
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Revista de Universidad y Sociedad del Conocimiento |
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RUSC |
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11 |
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192-207 |
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Admin @ si @ CMR2014 |
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2619 |
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David Sanchez-Mendoza; David Masip; Agata Lapedriza |
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Title |
Emotion recognition from mid-level features |
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Journal Article |
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2015 |
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Pattern Recognition Letters |
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PRL |
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67 |
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Part 1 |
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66–74 |
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Facial expression; Emotion recognition; Action units; Computer vision |
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In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. |
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Elsevier B.V. |
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0167-8655 |
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no |
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Admin @ si @ SML2015 |
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2746 |
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Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
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Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
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Journal Article |
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Year |
2016 |
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Computers & Industrial Engineering |
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CIE |
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94 |
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93-104 |
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Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
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In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one. |
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PERGAMON-ELSEVIER SCIENCE LTD |
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0360-8352 |
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OR;MV; |
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no |
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Admin @ si @ CFG2016 |
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2749 |
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