Wawan Setiawan, Yaya Wihardi, Enjun Junateti, Naufan Rusyda Faikar
Facial expression recognition is the process of identifying the expression that is displayed by a person. It can be used to evaluate the mood of students during a class so that can help teachers improve the learning goal achievement. However, the recognition process in real environments such as in classrooms is still a challenging problem due to different expressions and illumination under arbitrary poses. In this paper, we present a convolutional neural network-based method that combining with Gabor filter. The result shows that the proposed method can recognize three categories of student facial expressions that represent a good, bad, and neutral expression. Copyright © 2020 EAI
Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi 229, Bandung, Indonesia
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