Visualisation and prediction of conversation interest through mined social signals
Okwechime, D, Ong, E-J, Gilbert, A and Bowden, R (2011) Visualisation and prediction of conversation interest through mined social signals In: FG 2011: IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, 2011-03-21 - 2011-03-25, Santa Barbara, USA.
Available under License : See the attached licence file.
This paper introduces a novel approach to social behaviour recognition governed by the exchange of non-verbal cues between people. We conduct experiments to try and deduce distinct rules that dictate the social dynamics of people in a conversation, and utilise semi-supervised computer vision techniques to extract their social signals such as laughing and nodding. Data mining is used to deduce frequently occurring patterns of social trends between a speaker and listener in both interested and not interested social scenarios. The confidence values from rules are utilised to build a Social Dynamic Model (SDM), that can then be used for classification and visualisation. By visualising the rules generated in the SDM, we can analyse distinct social trends between an interested and not interested listener in a conversation. Results show that these distinctions can be applied generally and used to accurately predict conversational interest.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||https://doi.org/10.1109/FG.2011.5771380|
|Additional Information :||Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||31 May 2012 08:48|
|Last Modified :||23 Sep 2013 19:24|
Actions (login required)
Downloads per month over past year