Abstract
This paper provides us with a basic understanding of neural network-based facial emotion recognition. Communication is one of the main features of a community or a society, and emotions are an inevitable portion of any interpersonal communication. Emotions can be expressed in many different forms, which may or may not be observed with the naked eye. The facial emotion recognition system is a computer-based technology that uses deep learning algorithms to detect the faces instantaneously, codes the facial expressions, and concludes the emotional state of the person. In this study, we aim to extract facial expression using a deep convolutional neural network and classify a set of images to a target emotion class. The model inputs greyscale preprocessed images and was able to extract the facial expressions of a set of images and had classified them according to seven different target classes. This study was able to achieve a test accuracy of 97.4%.
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Saxena, S., Arora, M. (2021). Deep Neural Network-Based Facial Emotion Recognition. In: Kumar, N., Tibor, S., Sindhwani, R., Lee, J., Srivastava, P. (eds) Advances in Interdisciplinary Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9956-9_26
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DOI: https://doi.org/10.1007/978-981-15-9956-9_26
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