Abstract
Facial expressions consisting of various emotions play a significant role in interpersonal relations. Emotion detection from various expressions of the face can be performed broadly in three major steps which involve face detection-normalization, extraction of features and classification. An automated facial expression detection methodology has been introduced by the authors in this letter. Here, after face detection and normalization we extract three different types of facial features: Geometric, Texture and Structural. Based on these extracted features we employ SVM classifier to separate the face expressions which includes Happy, Sad, Disgust, Angry, Surprise and Fear. We have applied our algorithm on two databases: JAFFE and COHEN. We have successfully detected over 80% expressions from JAFFE and COHEN database.
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In this research, authors have used few human images; those are one of the authors of this paper and are given with his permission.
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Chattopadhyay, J., Kundu, S., Chakraborty, A., Banerjee, J.S. (2020). Facial Expression Recognition for Human Computer Interaction. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_119
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DOI: https://doi.org/10.1007/978-3-030-41862-5_119
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