@article{MAKHILLJEAS201914417457,
    title = {Automatic Recognition of Cognitive States Using Multimodal
Approaches in e-Learning Environments},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {4},
    pages = {1286-1294},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.1286.1294},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.1286.1294},
    author = {H.S. and},
    keywords = {Cognitive states,compressive sensing,facial expressions,hand-over face gesture,robust,hand},
    abstract = {Cognitive state recognition is one of the active science researches all around the world and it has
grown spontaneously in recent years. However, most research focuses on posed expressions, near frontal
recordings and they ignore eye gaze, head pose and considers hand occlusions as noise. It makes tough to tell
how the existing methods perform underneath conditions where faces appear in a wide range of poses and
occluded by hands. In this study, we propose multimodal approaches for building a real-time cognitive state
recognition system in e-Learning environments by integrating hand-over-face gesture with facial expression.
Our proposed system performs an average recognition rate of 90.51% with 15.8 fps is robust to variations in
facial expressions, hand shapes and occlusions.}
    }