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    On Gender Identification Using the Smile Dynamics

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    Thumbnail
    Publication date
    2017
    Author
    Al-dahoud, Ahmad
    Ugail, Hassan
    Keyword
    Smile analysis
    Gender classification
    Textureless features
    Motion and geometric features
    KNN
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    Gender classification has multiple applications including, but not limited to, face perception, age, ethnicity and identity analysis, video surveillance and smart human computer interaction. The majority of computer based gender classification algorithms analyse the appearance of facial features predominantly based on the texture of the static image of the face. In this paper, we propose a novel algorithm for gender classification using the smile dynamics without resorting to the use of any facial texture information. Our experiments suggest that this method has great potential for finding indicators of gender dimorphism. Our approach was tested on two databases, namely the CK+ and the MUG, consisting of a total of 80 subjects. As a result, using the KNN algorithm along with 10-fold cross validation, we achieve an accurate classification rate of 80% for gender simply based on the dynamics of a person's smile.
    URI
    http://hdl.handle.net/10454/14545
    Version
    No full-text in the repository
    Citation
    Al-dahoud A and Ugail H (2017) On Gender Identification Using the Smile Dynamics. In: Proceedings of the 2017 International Conference on Cyberworlds (CW). 20-22 Sep 2017, University of Chester, Chester, UK: 1-8.
    Link to publisher’s version
    https://doi.org/10.1109/CW.2017.26
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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