Rivaldo Tito Lamberto Da Silva, Aviv Yuniar Rahman, Fitri Marisa
Facial expressions can convey emotions or intentions such as sadness, happiness, surprise, fear, anger, or a bad mood. The meaning of facial expressions can be deduced from changes in the lips, eyes, cheeks, raised eyebrows, and open mouth, among other facial features. Human facial expressions can also communicate many messages, but in everyday life, most people do not realize or fail to understand the various human facial expressions. The majority of emotional expressions on faces happen so swiftly that people frequently get perplexed while trying to discriminate between the numerous emotional meanings they represent. This study aimed to accomplish the task of categorizing the facial expressions of children with special needs by employing a convolutional neural network (CNN) technique. The study's objective was to evaluate the accuracy of the CNNs in performing this task. An image of a child with special difficulties and various facial expressions was the study's object. Additionally, this research employs three distinct types of information: training, validation, and testing data. The technique utilized to examine images is CNN. This classification went through a performance test with Confusion Matrix and obtained a 97% precision, a 97% recall rate, and a 97% F1 score. This shows that the CNN method can classify © 2023 IEEE.
Universitas Widyagama Malang, Department of Informatic Engineering, Malang, Indonesia
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