Perception of drowsiness based on correlation with facial image features

Yugo Sato, Takuya Kato, Naoki Nozawa, Shigeo Morishima

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper presents a video-based method for detecting drowsiness. Generally, human beings can perceive their fatigue and drowsiness through looking at faces. The ability to perceive the fatigue and the drowsiness has been studied in many ways. The drowsiness detection method based on facial videos has been proposed [Nakamura et al. 2014]. In their method, a set of the facial features calculated with the Computer Vision techniques and the k-nearest neighbor algorithm are applied to classify drowsiness degree. However, the facial features that are ineffective against reproducing the perception of human beings with the machine learning method are not removed. This factor can decrease the detection accuracy.

    Original languageEnglish
    Title of host publicationProceedings of the ACM Symposium on Applied Perception, SAP 2016
    PublisherAssociation for Computing Machinery, Inc
    Pages139
    Number of pages1
    ISBN (Electronic)9781450343831
    DOIs
    Publication statusPublished - 2016 Jul 22
    EventACM Symposium on Applied Perception, SAP 2016 - Anaheim, United States
    Duration: 2016 Jul 222016 Jul 23

    Other

    OtherACM Symposium on Applied Perception, SAP 2016
    CountryUnited States
    CityAnaheim
    Period16/7/2216/7/23

      Fingerprint

    Keywords

    • Correlation coefficient
    • Drowsiness detection
    • Face evaluation
    • Feature learning
    • K-nearest neighbor algorithm

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Software
    • Applied Mathematics
    • Theoretical Computer Science

    Cite this

    Sato, Y., Kato, T., Nozawa, N., & Morishima, S. (2016). Perception of drowsiness based on correlation with facial image features. In Proceedings of the ACM Symposium on Applied Perception, SAP 2016 (pp. 139). Association for Computing Machinery, Inc. https://doi.org/10.1145/2931002.2947705