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

被引：0

作者：

Suwalak, Rattapong ^{[1
]}

Sukkaeo, Tuksina ^{[1
]}

Promwanrat, Thanawut ^{[1
]}

Satjawiso, Satjalinee ^{[1
]}

Werachattawan, Nisan ^{[2
]}

Pitanupong, Jarurin ^{[2
]}

机构：

[1] King Mongkuts Inst Technol Ladkrabang Prince, Dept Engn, Chumphon Campus, Chumphon, Thailand

[2] Prince Songkla Univ, Fac Med, Hat Yai, Thailand

来源：

2023 5TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS, ICCR | 2023年

关键词：

CNN; depressive disorder; face detection; facial expression; MTCNN;

D O I：

10.1109/ICCR60000.2023.10444800

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The 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.

引用

页码：185 / 188

页数：4

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