Abstract
Facial Expression Recognition (FER) has applications in various areas, including human behavior, human-machine interaction, mental disorder detection, human psychology, mood and response behavior, better interpersonal relationship, effective communication, video surveillance, face detection & tracking system, and real-time emotion detection system, etc. Therefore, processing and analysis of human faces is an active research area for human expression recognition. In this research work, through the parsing of face, a framework for human facial expression recognition has been proposed, in which seven facial expressions have been recognized, i.e., happy, sad, neutral, anger, disguise, fear, and surprise. A light-weighed CNN based model has been designed and tested on publically available datasets. To verify the robustness of the proposed model, several experiments have been performed. The proposed light weighted CNN based model achieved 95% result as per standard evaluation measure, i.e., accuracy. The proposed model of facial expression recognition can deal the subjects of any ethnicity.
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Gilanie, G., Rehman, N., Bajwa, U.I., Sharif, S., Ullah, H., Mushtaq, M.F. (2022). FERNet: A Convolutional Neural Networks Based Robust Model to Recognize Human Facial Expressions. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_35
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DOI: https://doi.org/10.1007/978-3-031-00828-3_35
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