Abstract
Emotions have a vital part in defining human mental health and social behaviour. Emotions are the most important form of non-verbal communication. Emotion detection has many applications in psychology, security, education, robotics, etc. Facial expressions are an important part in determining a person's emotion and thus can be studied. Different methods and approaches used for detecting emotions produce variable degrees of performance and accuracy depending on the data set used for training and testing. Keeping that in mind, in this paper, we have studied different approaches that can recognise the emotions of a person using facial expressions. Different pre-processing techniques were also studied which can affect the accuracy of a particular model. Several papers were reviewed in this paper and a few of them were analysed for their accuracy and performance. We have analysed that convolutional neural network (CNN)-based models performed better in comparison with some of the models. Among the CNN models, the mini-Xception model has performed robustly and provided unexpected results. A lot of research has been going on in this area, and while emotion recognition has improved over time, there is still room for improvement. The ultimate aim of these systems is to increase accuracy and efficiency. This achievement will have a positive impact in this domain.
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Mittal, S., Parashar, K., Belwal, P., Gahlaut, T. (2023). A Review of Different Approaches for Emotion Detection Based on Facial Expression Recognition. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_2
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DOI: https://doi.org/10.1007/978-981-99-4626-6_2
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