Academic Journal
Mental Health State Classification Using Facial Emotion Recognition and Detection
| Title: | Mental Health State Classification Using Facial Emotion Recognition and Detection |
|---|---|
| Authors: | Al-zanam, Adel Aref Ali, Hussein Alhomery, Omer Hussein Abdou Elsayed, Tan, Choo Peng |
| Source: | International Journal on Advanced Science, Engineering and Information Technology. 13:2274-2281 |
| Publisher Information: | Insight Society, 2023. |
| Publication Year: | 2023 |
| Subject Terms: | Artificial neural network, Artificial intelligence, Facial expression, Support vector machine, Feature (linguistics), Social Sciences, Experimental and Cognitive Psychology, Convolutional neural network, Pattern recognition (psychology), 7. Clean energy, QA273-280 Probabilities. Mathematical statistics, Field (mathematics), Machine learning, FOS: Mathematics, Psychology, 10. No inequality, Preprocessor, Perceptron, Pure mathematics, Linguistics, Computer science, FOS: Philosophy, ethics and religion, 3. Good health, FOS: Psychology, Facial Expression, Philosophy, Emotion Recognition, Affective Computing, Emotion classification, FOS: Languages and literature, Feature extraction, Mathematics, Emotion Recognition and Analysis in Multimodal Data |
| Description: | Analyzing and understanding emotion can help in various aspects, such as realizing one’s attitude, behavior, etc. By understanding one’s emotions, one's mental health state can be calculated, which can help in the medical field by classifying whether one is mentally stable or not. Facial Recognition is one of the many fields of computer vision that utilizes convolutional networks or Conv Nets to perform, train, and learn. Conv Nets and other machine learning algorithms have evolved to adapt better to larger datasets. One of the advancements in Conv Nets and machines is the introduction of various Conv architectures like VGGNet. Thus, this study will present a mental health state classification approach based on facial emotion recognition. The methodology comprises several interconnected components, including preprocessing, feature extraction using Principal Component Analysis (PCA) and VGGNet, and classification using Support Vector Machines (SVM) and Multilayer Perceptron (MLP). The FER2013 dataset tests multiple models’ performances, and the best model is employed in the mental health state classification. The best model, which combines Visual Geometry Group Network (VGGNet) feature extraction with SVM classification, achieved an accuracy of 66%, demonstrating the effectiveness of the proposed methodology. By leveraging facial emotion recognition and machine learning techniques, the study aims to develop an effective method. |
| Document Type: | Article Other literature type |
| File Description: | text |
| ISSN: | 2460-6952 2088-5334 |
| DOI: | 10.18517/ijaseit.v13i6.19055 |
| DOI: | 10.18517/ijaseit.13.6.19055 |
| DOI: | 10.60692/nz9c9-rzc70 |
| DOI: | 10.60692/0c6rp-tkk11 |
| DOI: | 10.60692/d0hkf-xa677 |
| DOI: | 10.60692/4769n-60349 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....5b6b51a727ea4ad1da52ddc9da078fa1 |
| Database: | OpenAIRE |
| ISSN: | 24606952 20885334 |
|---|---|
| DOI: | 10.18517/ijaseit.v13i6.19055 |