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.
Original language | English |
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Pages (from-to) | 866-876 |
Number of pages | 11 |
Journal | Neurocomputing |
Volume | 175 |
DOIs | |
State | Published - 2016 |
Keywords
- Dyadic social interaction analysis
- Head gestures recognition
- Mirroring detection
- Wearable devices