ABSTRACT
In this paper, we propose an effective method for emergent leader detection in meeting environments which is based on nonverbal visual features. Identifying emergent leader is an important issue for organizations. It is also a well-investigated topic in social psychology while a relatively new problem in social signal processing (SSP). The effectiveness of nonverbal features have been shown by many previous SSP studies. In general, the nonverbal video-based features were not more effective compared to audio-based features although, their fusion generally improved the overall performance. However, in absence of audio sensors, the accurate detection of social interactions is still crucial. Motivating from that, we propose novel, automatically extracted, nonverbal features to identify the emergent leadership. The extracted nonverbal features were based on automatically estimated visual focus of attention which is based on head pose. The evaluation of the proposed method and the defined features were realized using a new dataset which is firstly introduced in this paper including its design, collection and annotation. The effectiveness of the features and the method were also compared with many state of the art features and methods.
- N. Ambady, F. Bernieri, and J. Richeson. Toward a histology of social behavior: Judgmental accuracy from thin slices of the behavioral stream. Advances in Experimental Social Psychology, 32:201–257, 2000.Google ScholarCross Ref
- O. Aran and D. Gatica-Perez. Fusing audio-visual nonverbal cues to detect dominant people in small group conversations. In ICPR, pages 3687–3690, 2010. Google ScholarDigital Library
- S. O. Ba and J.-M. Odobez. Recognizing people’s focus of attention from head poses: a study. IDIAP Research Report 06-42, pages 1–27, 2006.Google Scholar
- R. Bales. SYMLOG: case study kit with instructions for a group self study. The Free Press, New York, 1980.Google Scholar
- T. Baltruˇ saitis, P. Robinson, and L.-P. Morency. Constrained local neural fields for robust facial landmark detection in the wild. In IEEE ICCVW 300 Faces in-the-Wild Challenge, pages –, 2013.Google Scholar
- C. Beyan and R. Fisher. Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognition, 48(5):1653–1672, 2015. Google ScholarDigital Library
- S. Bilakhia, S. Petridis, A. Nijholt, and M. Pantic. The mahnob mimicry database - a database of naturalistic human interactions. Pattern Recognition Letters, 66:52–61, 2015. Google ScholarDigital Library
- J. Carletta, S. Ashby, S. Bourban, M. Flynn, M. Guillemot, T. Hain, J. Kadlec, V. Karaiskos, W. Kraaij, and M. Kronenthal. The AMI meeting corpus: A pre-announcement. In MLMI, pages 28–39, June 2005. Google ScholarDigital Library
- D. R. Carney, J. A. Hall, and L. S. LeBeau. Beliefs about the nonverbal expression of social power. Journal of Nonverbal Behavior, 29(2):105–122, 2005.Google ScholarCross Ref
- V. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer. Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, 2002. Google ScholarCross Ref
- D. Cristinacce and T.F.Cootes. Feature detection and tracking with constrained local models. In BMVC, pages 929–938, 2006.Google ScholarCross Ref
- S. Feese, A. Muaremi, B. Arnrich, G. Troster, B. Meyer, and K. Jonas. Discriminating individually considerate and authoritarian leaders by speech activity cues. In IEEE PASSAT, and IEEE SocialCom, pages 1460–1465, 2011.Google Scholar
- G. Fumera and F. Roli. Cost-sensitive learning in support vector machines. In the Workshop Mach. Learn. Meth. Appl., pages –, 2002.Google Scholar
- D. Gatica-Perez, I. McCowan, D. Zhang, and S. Bengio. Detecting group interest level in meetings. In IEEE ICASSP, pages 489–492, 2005.Google ScholarCross Ref
- J. A. Hall, L. S. LeBeau, and E. J. Coats. Nonverbal behavior and the vertical dimension of social relations: A meta-analysis. Psychological Bulletin, 131(6):898–924, 2005.Google ScholarCross Ref
- D. Hansen and Q. Ji. In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell., 32(3):478–500, 2010. Google ScholarDigital Library
- A. Hare, R. Polley, and P. Stone. The Symlog Practitioner: Applications of Small Group Research. Praeger Press, New York, 1998.Google Scholar
- H. Hung, Y. Huang, G. Friedland, and D. Gatica-Perez. Estimating dominance in multi-party meetings using speaker diarization. IEEE Trans. Audio, Speech, Language Process, 19(4):847–860, May 2011. Google ScholarDigital Library
- H. Hung, D. B. Jayagopi, S. Ba, J.-M. Odobez, and D. Gatica-Perez. Investigating automatic dominance estimation in groups from visual attention and speaking activity. In ICMI, pages 233–236, 2008. Google ScholarDigital Library
- D. Jayagopi, H. Hung, C. Yeo, and D. Gatica-Perez. Modeling dominance in group conversations from nonverbal activity cues. IEEE Trans. Audio, Speech, Language Process., Sp. Issue on Multimodal Processing for Speech-based Interactions, 17(3):501–513, 2009.Google ScholarDigital Library
- D. Johnson and F. Johnson. Joining together: Group theory and group skills. Prentice-Hall, Inc., 1991.Google Scholar
- N. Jovanovic, R. op den Akker, and A. Nijholt. Addressee identification in face-to-face meetings. In EACL, pages 169–176, 2006.Google Scholar
- M. L. Knapp, J. A. Hall, and T. G. Horgan. Nonverbal Communication in Human Interaction. 8th Edition, Wadsworth, Cengage Learning, Boston, 2013.Google Scholar
- R. Koenigs. SYMLOG reliability and validity. San Diego: SYMLOG Consulting Group, 1999.Google Scholar
- R. Lord, R. Foti, and C. D. Vader. A test of leadership categorization theory: Internal structure, information processing, and leadership perceptions. Organizational behavior and human performance, 34(3):343–378, 1984.Google Scholar
- M. Marin-Jimenez, A. Zisserman, and V. Ferrari. Here’s looking at you, kid. detecting people looking at each other in videos. In BMVC, pages –, 2011.Google Scholar
- I. McCowan, D. Gatica-Perez, S. Bengio, G. Lathoud, M. Barnard, and D. Zhang. Automatic analysis of multimodal group actions in meetings. IEEE Trans. Pattern Anal. Mach. Intell., 27(3):305–317, March 2005. Google ScholarDigital Library
- T. Meservy, M. Jensen, J. Kruse, J. Burgoon, J. Nunamaker, D. Twitchell, G. Tsechpenakis, and D. Metaxas. Deception detection through automatic, unobtrusive analysis of nonverbal behavior. IEEE Intelligent Systems, 20(5):36–43, 2005. Google ScholarDigital Library
- N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man, Cybern., Syst., 9(1):62–66, 1979.Google ScholarCross Ref
- K. Otsuka, H. Sawada, and J. Yamato. Automatic inference of cross-modal nonverbal interactions in multiparty conversations. In ICMI, pages 255–262, 2007. Google ScholarDigital Library
- D. Sanchez-Cortes. Computational methods for audio-visual analysis of emergent leadership. PhD Thesis, EPFL, Lausanne, pages –, 2013.Google Scholar
- D. Sanchez-Cortes, O. Aran, D. B. Jayagopi, M. S. Mast, and D. Gatica-Perez. Emergent leaders through looking and speaking: from audio-visual data to multimodal recognition. Journal on Multimodal User Interfaces, 7(1–2):39–53, August 2012.Google ScholarCross Ref
- D. Sanchez-Cortes, O. Aran, M. S. Mast, and D. Gatica-Perez. A nonverbal behavior approach to identify emergent leaders in small groups. IEEE Trans. On Multimedia, 14(3):816–832, 2012. Google ScholarDigital Library
- R. Stiefelhagen, J. Yang, and A. Waibel. Modeling focus of attention for meeting indexing based on multiple cues. IEEE Trans. on Neural Networks, 13(4):928–938, 2002. Google ScholarDigital Library
- R. Subramanian, J. Staiano, K. Kalimeri, N. Sebe, and F. Pianesi. Putting the pieces together: multimodal analysis of social attention in meetings. In ACM Multimedia, pages 25–29, October 2010. Google ScholarDigital Library
- S. Vascon, E. Z. Mequanint, M. Cristani, H. Hung, M. Pelillo, and V. Murino. Detecting conversational groups in images and sequences: A robust game-theoretic approach. CVIU, 143:11–24, 2016. Google ScholarDigital Library
- B. Yap, K. Rani, H. Rahman, S. Fong, Z. Khairudin, and N. Abdullah. An application of oversampling, undersampling, bagging, and boosting in handing imbalanced datasets. In DaEng, Lecture Notes in Electrical Engineering, 285:13–22, 2014.Google ScholarCross Ref
Index Terms
- Detecting emergent leader in a meeting environment using nonverbal visual features only
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