title: Video-based detection of the clinical depression in adolescents.
abstract: 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.
authors: RMIT University, Melbourne, Australia. namunu@maddage@rmit.edu
authors: Namunu C
authors: NC
authors: Maddage
authors: Rajinda
authors: R
authors: Senaratne
authors: Lu-Shih Alex
authors: LS
authors: Low
authors: Margaret
authors: M
authors: Lech
authors: Nicholas
authors: N
authors: Allen
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D003863:Depression / Q000175:diagnosis* / Q000473:pathology*; D005145:Face; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012737:Sex Factors; D014741:Video Recording