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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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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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Admin @ si @ MNT2014 |
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2453 |
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Manuel Graña; Bogdan Raducanu |
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Special Issue on Bioinspired and knowledge based techniques and applications |
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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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Admin @ si @ GrR2015 |
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2598 |
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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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1698-580X |
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OR;MV |
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Admin @ si @ CMR2014 |
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2619 |
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Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza de Luna; Joaquin Salas |
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Title |
Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices |
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2016 |
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Neurocomputing |
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NEUCOM |
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175 |
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B |
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866–876 |
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Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices |
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During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. |
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OR; 600.072; 600.068;MV |
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no |
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Admin @ si @ TRM2016 |
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2721 |
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Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas |
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Title |
Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology |
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2016 |
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IEEE Transactions on Affective Computing |
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TAC |
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9 |
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2 |
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161-175 |
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Mirroring; Nodding; Competence; Perception; Wearable Technology |
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Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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OR; 600.072;MV |
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Admin @ si @ MTR2016 |
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2826 |
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