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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.13753 |
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| _version_ | 1866915551517868032 |
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| author | Parikh, Aditya Sadeghi, Misha Richer, Robert Rupp, Lydia Helene Schindler-Gmelch, Lena Keinert, Marie Hager, Malin Capito, Klara Rahimi, Farnaz Egger, Bernhard Berking, Matthias Eskofier, Bjoern M. |
| author_facet | Parikh, Aditya Sadeghi, Misha Richer, Robert Rupp, Lydia Helene Schindler-Gmelch, Lena Keinert, Marie Hager, Malin Capito, Klara Rahimi, Farnaz Egger, Bernhard Berking, Matthias Eskofier, Bjoern M. |
| contents | Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disorder. Traditional diagnostic methods rely on subjective assessments, necessitating objective approaches for accurate diagnosis. Our study investigates the use of facial action units (AUs) and emotions as biomarkers for depression. We analyzed facial expressions from video data of participants classified with or without depression. Our methodology involved detailed feature extraction, mean intensity comparisons of key AUs, and the application of time series classification models. Furthermore, we employed Principal Component Analysis (PCA) and various clustering algorithms to explore the variability in emotional expression patterns. Results indicate significant differences in the intensities of AUs associated with sadness and happiness between the groups, highlighting the potential of facial analysis in depression assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13753 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units Parikh, Aditya Sadeghi, Misha Richer, Robert Rupp, Lydia Helene Schindler-Gmelch, Lena Keinert, Marie Hager, Malin Capito, Klara Rahimi, Farnaz Egger, Bernhard Berking, Matthias Eskofier, Bjoern M. Computer Vision and Pattern Recognition Depression is characterized by persistent sadness and loss of interest, significantly impairing daily functioning and now a widespread mental disorder. Traditional diagnostic methods rely on subjective assessments, necessitating objective approaches for accurate diagnosis. Our study investigates the use of facial action units (AUs) and emotions as biomarkers for depression. We analyzed facial expressions from video data of participants classified with or without depression. Our methodology involved detailed feature extraction, mean intensity comparisons of key AUs, and the application of time series classification models. Furthermore, we employed Principal Component Analysis (PCA) and various clustering algorithms to explore the variability in emotional expression patterns. Results indicate significant differences in the intensities of AUs associated with sadness and happiness between the groups, highlighting the potential of facial analysis in depression assessment. |
| title | Exploring Facial Biomarkers for Depression through Temporal Analysis of Action Units |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.13753 |