Facial Emotion Recognition and Detection Using CNN
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Abstract
People's facial expressions reveal a common set of feelings that we all share. Face-recognition
technology has been deployed in a wide range of applications that require additional security or personal information. Facial expressions such as sadness, happiness, surprise, rage, and fear may all be used to determine a person's emotional state using facial emotion detection. Face emotion recognition and detection is critical for marketing purposes. Customers' reactions to all of a company's products and offerings are the lifeblood of the majority of enterprises. It is possible to determine whether or not a consumer is satisfied with a product or service based on their emotional response to an image or video captured by an artificially intelligent system. Using transformed photos, several machine learning approaches, such as Random forest and SVM, were previously utilised to estimate sentiment. Computer vision, for example, has made significant strides in recent years thanks to advancements made possible by deep learning. Facial expressions may be detected using
a convolutional neural network (CNN) model. This dataset is used for both training and testing purposes
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