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    Adaptive Classification of Occluded Facial Expressions of Affective States

    Access Status
    Fulltext not available
    Authors
    Vice, Jordan
    Khan, Masood
    Murray, Iain
    Yanushkevich, Svetlana
    Date
    2022
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Vice, J. and Khan, M. and Murray, I. and Yanushkevich, S. 2022. Adaptive Classification of Occluded Facial Expressions of Affective States. In: 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems, 25th May 2022, Larnaca, Cyprus.
    Source Title
    Proceedings of the 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems
    Source Conference
    2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems
    DOI
    10.1109/EAIS51927.2022.9787693
    ISBN
    978-1-6654-3706-6
    Faculty
    Faculty of Science and Engineering
    School
    School of Civil and Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/88712
    Collection
    • Curtin Research Publications
    Abstract

    Internationally, the recent pandemic caused severe social changes forcing people to adopt new practices in their daily lives. One of these changes requires people to wear masks in public spaces to mitigate the spread of viral diseases. Affective state assessment (ASA) systems that rely on facial expression analysis become impaired and less effective due to the presence of visual occlusions caused by wearing masks. Therefore, ASA systems need to be future-proofed and equipped with adaptive technologies to be able to analyze and assess occluded facial expressions, particularly in the presence of masks. This paper presents an adaptive approach for classifying occluded facial expressions when human faces are partially covered with masks. We deployed an unsupervised, cosine similarity-based clustering approach exploiting the continuous nature of the extended Cohn-Kanade (CK+) dataset. The cosine similaritybased clustering resulted in twenty-one micro-expression clusters that describe minor variations of human facial expressions. Linear discriminant analysis was used to project all clusters onto lower-dimensional discriminant feature spaces, allowing for binary occlusion classification and the dynamic assessment of affective states. During the validation stage, we observed 100% accuracy when classifying faces with features extracted from the lower part of the occluded faces (occlusion detection). We observed 76.11% facial expression classification accuracy when features were gathered from the uncovered full-faces and 73.63% classification accuracy when classifying upper-facial expressions - applied when the lower part of the face is occluded. The presented system promises an improvement to visual inspection systems through an adaptive occlusion detection and facial expression classification framework.

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