expressions. Identifying Student Mood Based on Facial Expressions by Using Discrete Wavelet Transform and Fuzzy K-Nearest Neighbor
identification of student expressions
Mood is a temporary emotional state. Mood usually has positive or negative quality values. Emotional intelligence has a role of more than 80% in achieving life success and is one of the factors that influence the students' capture power in the lecture process. By knowing the emotions of students, we can help capture students' ability during the lecture process, and the need for a system that can identify emotions that are formed during lectures.
This system is built using the Discrete Wavelet Transform which transforms the image into 4 sub-images. The image of Discrete Wavelet Transform results looks rough or forms a face that can distinguish student expressions. The results of the Discrete Wavelet Transform image processing are classified using Fuzzy K-nearest neighbor. Classification is divided into three expressions, namely: Angry, Happy and Sad with accuracy of 77.49%
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