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Jordi Vitria; M. Bressan; Petia Radeva |
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Bayesian classification of cork stoppers using class-conditional independent component analysis |
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2007 |
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IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482) |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VBR2007 |
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795 |
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Fadi Dornaika; Bogdan Raducanu |
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Inferring Facial Expressions from Videos: Tool and Application |
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2007 |
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Signal Processing: Image Communication, vol. 22(9):769–784 |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2007b |
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918 |
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Matthias S. Keil; Agata Lapedriza; David Masip; Jordi Vitria |
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Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine |
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2008 |
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PLoS ONE 3(7):e2590, DOI:10.1371/journal.pone.0002590 |
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OR;MV |
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BCNPCL @ bcnpcl @ KLM2008 |
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978 |
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Fadi Dornaika; Bogdan Raducanu |
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3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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2008 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2008d |
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1109 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera |
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Title |
Automatic Detection of Dominance and Expected Interest |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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12 |
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Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems. |
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1110-8657 |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ EPR2010d |
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1283 |
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