Maddage Namunu C, Senaratne Rajinda, Low Lu-Shih Alex, Lech Margaret, Allen Nicholas
RMIT University, Melbourne, Australia. namunu@
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3723-6. doi: 10.1109/IEMBS.2009.5334815.
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.
我们提出了一个用于检测抑郁和非抑郁受试者视频内容的框架。首先，我们使用在面部地标处提取的Gabor小波特征来表征视频流中表达的情绪，这些面部地标是通过地标模型匹配算法检测到的。使用高斯混合模型构建抑郁和非抑郁类别模型。我们使用8小时的视频记录（每个受试者1小时视频记录），且性别和类别均保持平衡，研究了基于性别的建模方法和与性别无关的建模方法在抑郁和非抑郁内容分类方面的有效性。我们发现，与性别无关的建模方法相比，基于性别的内容建模方法将分类准确率提高了6%，平均准确率达到78.6%。
