Face Detection Approach to Classify Emotions Based on Facial Expression in Depressive Disorder

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dc.contributor.authorSuwalak, Rattapong
dc.contributor.authorSukkaeo, Tuksina
dc.contributor.authorPromwanrat, Thanawut
dc.contributor.authorSatjawiso, Satjalinee
dc.contributor.authorWerachattawan, Nisan
dc.contributor.authorPitanupong, Jarurin
dc.date.accessioned2026-08-06T10:38:43Z
dc.date.available2026-08-06T10:38:43Z
dc.date.issued2023-01-01
dc.description.abstractThe Multi-Task Cascaded Convolution Neural Network (MTCNN) is presented in this paper to classify the emotion and generate the facial dots as a representative of the patient. In depressive disorder diagnosis, the facial expressions can be used to observe the behavior of the patient. From the results, the system can be classified the emotion into 5-class i.e., happy, angry, disgusted, neutral, and surprised. For emotions of happy, angry, neutral, and surprised, the accuracy is more than 98 %, and for disgusted emotion is 96 %. Furthermore, the system can generate the real-time facial dots for emotion classification. Therefore, it can be a candidate to apply to collect and analyze the emotions of the patient under the privacy policy in a depressive disorder.
dc.identifier.citation2023 5th International Conference on Control and Robotics Iccr 2023, 185-188, 2023
dc.identifier.doi10.1109/ICCR60000.2023.10444800
dc.identifier.other2-s2.0-85187203975
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13671
dc.source2023 5th International Conference on Control and Robotics Iccr 2023
dc.subjectCNN
dc.subjectdepressive disorder
dc.subjectface detection
dc.subjectfacial expression
dc.subjectMTCNN
dc.titleFace Detection Approach to Classify Emotions Based on Facial Expression in Depressive Disorder
dc.typeConference Paper

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