A novel dataset for real-life evaluation of facial expression recognition methodologies

Muhammad Hameed Siddiqi, Maqbool Ali, Muhammad Idris, Oresti Banos Legran, Sungyoung Lee, Hyunseung Choo

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    Abstract

    One limitation seen among most of the previous methods is that they were evaluated under settings that are far from real-life scenarios. The reason is that the existing facial expression recognition (FER) datasets are mostly pose-based and assume a predefined setup. The expressions in these datasets are recorded using a fixed camera deployment with a constant background and static ambient settings. In a real-life scenario, FER systems are expected to deal with changing ambient conditions, dynamic background, varying camera angles, different face size, and other human-related variations. Accordingly, in this work, three FER datasets are collected over a period of six months, keeping in view the limitations of existing datasets. These datasets are collected from YouTube, real world talk shows, and real world interviews. The most widely used FER methodologies are implemented, and evaluated using these datasets to analyze their performance in real-life situations.
    Original languageUndefined
    Title of host publication29th Canadian Conference on Artificial Intelligence, CCAI 2016
    Place of PublicationBerlin
    PublisherSpringer
    Pages89-95
    Number of pages7
    ISBN (Print)978-3-319-34110-1
    DOIs
    Publication statusPublished - 31 May 2016

    Publication series

    NameLecture notes in artificial intelligence
    PublisherSpringer
    Volume9673

    Keywords

    • YouTube
    • Facial expression recognition
    • Feature Selection
    • Feature extraction
    • Recognition
    • METIS-318533
    • IR-101596
    • Real-world
    • EWI-27248

    Cite this

    Siddiqi, M. H., Ali, M., Idris, M., Banos Legran, O., Lee, S., & Choo, H. (2016). A novel dataset for real-life evaluation of facial expression recognition methodologies. In 29th Canadian Conference on Artificial Intelligence, CCAI 2016 (pp. 89-95). (Lecture notes in artificial intelligence; Vol. 9673). Berlin: Springer. https://doi.org/10.1007/978-3-319-34111-8_12