Abstract
Human communication relies heavily on facial expressions. Although detecting emotion from facial expression has always been a simple task for humans, doing so with a computer technique is rather difficult. It is now possible to discern emotions from images because of recent advances in computer vision and machine learning. They frequently disclose people’s true emotional situations beyond their spoken language. Furthermore, visual pattern-based understanding of human effect is a critical component of any human-machine interaction system, which is why the task of Facial Expression Recognition (FER) attracts both scientific and corporate interest. Deep Learning (DL) approaches have recently achieved very high performance on FER by utilizing several architectures and learning paradigms. We have considered two types of images here, mainly static and live streaming datasets, and compared their performance using a convolution neural networks (CNN) strategy.
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Seal, A., Saha, R., Kumar, R., Goenka, S., Dey, L. (2022). Facial Emotion Recognition in Static and Live Streaming Image Dataset Using CNN. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_23
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