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Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza de Luna; Joaquin Salas |
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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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Abstract |
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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Admin @ si @ TRM2016 |
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2721 |
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Author |
Rozenn Dhayot; Fernando Vilariño; Gerard Lacey |
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Title |
Improving the Quality of Color Colonoscopy Videos |
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2008 |
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EURASIP Journal on Image and Video Processing |
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EURASIP JIVP |
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139429 |
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1 |
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1-9 |
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800 |
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MV;SIAI |
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fernando @ fernando @ |
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2422 |
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M. Bressan; Jordi Vitria |
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Title |
Independent Component Analysis and Naïve Bayes Classification. |
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2002 |
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Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501. |
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OR;MV |
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BCNPCL @ bcnpcl @ BrV2002a |
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288 |
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M. Bressan; Jordi Vitria |
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Independent Feature Selection |
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2003 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): 1312–1317 (IF: 3.823) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ BrV2003a |
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366 |
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Author |
Bogdan Raducanu; D. Gatica-Perez |
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Title |
Inferring competitive role patterns in reality TV show through nonverbal analysis |
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2012 |
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Multimedia Tools and Applications |
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MTAP |
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56 |
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1 |
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207-226 |
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This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority. |
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Elsevier |
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1380-7501 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaG2012 |
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1360 |
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