Video-based detection of the clinical depression in adolescents
| Autor: | Lu-Shih Alex Low, Rajinda Senaratne, Margaret Lech, Nicholas B. Allen, Namunu C. Maddage |
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| Rok vydání: | 2009 |
| Předmět: |
Male
Adolescent Computer science Speech recognition Feature extraction Normal Distribution Video Recording ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern Recognition Automated Sex Factors Artificial Intelligence Active shape model Image Interpretation Computer-Assisted Humans Child Face detection Contextual image classification Depression business.industry Gabor wavelet Supervised learning Reproducibility of Results Pattern recognition Mixture model Class (biology) ComputingMethodologies_PATTERNRECOGNITION Face Female Artificial intelligence business Algorithms |
| Zdroj: | 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. |
| DOI: | 10.1109/iembs.2009.5334815 |
| Popis: | We proposed a framework to detect the video contents of depressed and non-depressed subjects. First we characterized the expressed emotions in the video stream using Gabor wavelet features extracted at the facial landmarks which were detected using landmark model matching algorithm. Depressed and non-depressed class models were constructed using Gaussian Mixture models. Using 8 hours of video recordings, an hour of video recording per subject, and both gender and class balanced, we examined the effectiveness of both gender based and gender independent modeling approaches for depressed and non-depressed content classification. We found that the gender based content modeling approach improved the classification accuracy by 6% compared to the gender independent modeling approach, achieving 78.6% average accuracy. |
| Databáze: | OpenAIRE |
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