Abstract
The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.
Highlights
Facial expression is a nonverbal way of communication among humans
Facial expression recognition is utilized in a variety of applications, including the identification of mental disorders, depression analysis, and health forecasting and criminal detection. e seven universal facial expressions that are recognized in humans are “Happy, Sad, Fear, Anger, Surprise, Disgust, and Neutral.” is study focused on the classification of aforementioned facial expression with the help of deep learning techniques
We provide a strategy for facial expression recognition based on deep learning and CNN to overcome difficulties that commonly occur, such as low recognition accuracy and weak generalisation capacity of traditional face expression recognition algorithms. is method demonstrates the CNN model’s capacity to recognise patient facial expressions more accurately
Summary
Facial expression is a nonverbal way of communication among humans. E technique of recognizing a person’s facial expression category is known as facial expression recognition. E seven universal facial expressions that are recognized in humans are “Happy, Sad, Fear, Anger, Surprise, Disgust, and Neutral.” is study focused on the classification of aforementioned facial expression with the help of deep learning techniques. Several techniques have been devised for automatic facial expression recognition with the help of deep neural networks. Deep neural networks replicate the neocortex of the human brain that has several neurons. Ese neurons are used to build the neural network in deep learning models. Deep learning has various types of neural network models [2,3,4]
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