Abstract
Communication between people relies on human expression more than any other factor. Emotional identification is, right now, a hot study area. Typically, people will show seven distinct feelings (happy, angry, surprised, sad, disgusted, fearful, and neutral). Humans communicate their feelings via words, body language, and facial expressions. Humans’ ability to reveal inner thought processes via facial expressions is crucial to studying human behavior. Many fields use facial expression analysis, including those concerned with increased security, automated criminal identification, the diagnosis of mental illness, and communication between people and computers. Most studies on emotion detection have only used straightforward CNN and RNN models. However, training such a big data set may take a long because the models need a vast data set. To this end, we suggested a model that combines the Mobile Net-V2 model with the transfer learning strategy to speed up the generation time and improve the accuracy of emotion detection. In order to improve upon the current method, we have compiled a large CIFE data collection from the relevant literature. One of the best outcomes for emotion detection is attained by the suggested model architecture when tested on the data mentioned earlier. Based on what we found, the new system should be able to identify more accurately than the old one.
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Ahammed, M.T., Ghosh, S., Rahman, M.A., Chandra, P., Shuvo, A.I., Balaji, P. (2023). Meta-transfer Learning for Contextual Emotion Detection in Face Affirmation. In: Tiwari, R.K., Sahoo, G. (eds) Recent Trends in Artificial Intelligence and IoT. ICAII 2023. Communications in Computer and Information Science, vol 1822. Springer, Cham. https://doi.org/10.1007/978-3-031-37303-9_9
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