Abstract
Recognition of human feelings is among the most tedious tasks using computer vision with immense scope. Currently, about 450 million individuals suffer from psychiatric diseases, putting psychiatric illnesses among the world's leading causes of ill health and impairment treatments, which are accessible, but nearly two-thirds of individuals with a recognized psychiatric illness do not seek help from a mental healthcare professional. Although human beings are extremely complex species when it comes to emotions, but facial expressions can be utilized to approximate the emotion of user. This paper focuses on various available algorithms and approaches to recognize human facial emotion and compare the efficiency of various methods. Although there are six basic emotions—anger, disgust, fear, happy, sad, surprise—but human beings constantly experience combination of these emotions, thus making it difficult to pinpoint a single emotional state accurately. In this paper, an attempt is made to compare accuracy to recognize five human emotions—anger, happy, neutral, sad and surprise by observing facial muscle movement through computer vision and predicting emotion with the help of machine learning and deep learning models. The performance efficiency is measured simply by providing each model with fixed number of input images and comparing correct number of outputs for each model. Finally, a comparison between different machine learning and deep learning-based models is made based upon the accuracy score for each model.
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Aggarwal, A., Garg, S., Madaan, R., Kumar, R. (2021). Comparison of Different Machine Learning and Deep Learning Emotion Detection Models. In: Singh, B., Coello Coello, C.A., Jindal, P., Verma, P. (eds) Intelligent Computing and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1295-4_41
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