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    Automated analysis of interactional synchrony using robust facial tracking and expression recognition

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    Name:
    YuZhangYuDunbarJensenBurgoonMe ...
    Size:
    902.4Kb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Yu, Xiang
    Zhang, Shaoting
    Yu, Yang
    Dunbar, Norah
    Jensen, Matthew
    Burgoon, Judee K.
    Metaxas, Dimitris N.
    Issue Date
    2013-04
    Keywords
    Face
    Shape
    Vectors
    Feature extraction
    Accuracy
    Correlation
    Visualization
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    X. Yu et al., "Automated analysis of interactional synchrony using robust facial tracking and expression recognition," 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, 2013, pp. 1-6, doi: 10.1109/FG.2013.6553802.
    Journal
    2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
    Rights
    Copyright © 2013, IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    In this paper, we propose an automated, data-driven and unobtrusive framework to analyze interactional synchrony. We use this information to determine whether interpersonal synchrony can be an indicator of deceit. Our framework includes a robust facial tracking module, an effective expression recognition method, synchrony feature extraction and feature selection methods. These synchrony features are used to learn classification models for the deception recognition. To evaluate our proposed framework, we have conducted extensive experiments on a database of 242 video samples. We validate the performance of each technical module in our framework, and also show that these synchrony features are very effective at detecting deception.
    eISBN
    9781467355469
    DOI
    10.1109/fg.2013.6553802
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1109/fg.2013.6553802
    Scopus Count
    Collections
    UA Faculty Publications

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