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VIVAFER - voluntary and involuntary actions-based facial expression and recognition

Author

Listed:
  • Annadurai Swaminathan
  • Michael Arock

Abstract

Facial expression recognition (FER) and analysis play a vital role in developing emotion and FER-based applications. In past research, anger, surprise, contempt, happy, disgust, sad, fear and neutral were the basic emotions usually inferred from FER. Humans, also exhibit some facial expressions based on voluntary and involuntary actions (VIVA), which do not infer basic emotions. VIVAFER must be considered as essential expressions when developing an application. Otherwise, if a yawn (a class in VIVAFER) is considered as 'surprised', then the application becomes meaningless. Very few researchers dealt with this area, which is also limited to specific applications. Considering the above problem, the present work broadly classifies FER based on VIVA. For research purposes, a new dataset named VIVAFER is constructed. Various machine and deep learning algorithms are used for training, and testing of these 15 classes. The applications of VIVAFER are behavioural analysis, feedback system, medical-applications, patient monitoring system, etc.

Suggested Citation

  • Annadurai Swaminathan & Michael Arock, 2021. "VIVAFER - voluntary and involuntary actions-based facial expression and recognition," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 26(4), pages 467-487.
    • Handle: RePEc:ids:ijbire:v:26:y:2021:i:4:p:467-487
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