Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis.
|Title of host publication||2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2013|
|Event||2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 - Shanghai, China|
Duration: 22 Apr 2013 → 26 Apr 2013
|Conference||2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013|
|Period||22/04/13 → 26/04/13|