DocumentCode
724683
Title
Cross-cultural detection of depression from nonverbal behaviour
Author
Alghowinem, Sharifa ; Goecke, Roland ; Cohn, Jeffrey F. ; Wagner, Michael ; Parker, Gordon ; Breakspear, Michael
Author_Institution
Res. Sch. of Comput. Sci., Australian Nat. Univ., Canberra, ACT, Australia
fYear
2015
fDate
4-8 May 2015
Firstpage
1
Lastpage
8
Abstract
Millions of people worldwide suffer from depression. Do commonalities exist in their nonverbal behavior that would enable cross-culturally viable screening and assessment of severity? We investigated the generalisability of an approach to detect depression severity cross-culturally using video-recorded clinical interviews from Australia, the USA and Germany. The material varied in type of interview, subtypes of depression and inclusion healthy control subjects, cultural background, and recording environment. The analysis focussed on temporal features of participants´ eye gaze and head pose. Several approaches to training and testing within and between datasets were evaluated. The strongest results were found for training across all datasets and testing across datasets using leave-one-subject-out cross-validation. In contrast, generalisability was attenuated when training on only one or two of the three datasets and testing on subjects from the dataset(s) not used in training. These findings highlight the importance of using training data exhibiting the expected range of variability.
Keywords
cultural aspects; learning (artificial intelligence); psychology; video signal processing; Australia; Germany; USA; cross-cultural depression detection; cross-culturally viable screening; nonverbal behaviour; training data; video-recorded clinical interviews; Australia; Feature extraction; Interviews; Magnetic heads; Support vector machines; Testing; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on
Conference_Location
Ljubljana
Type
conf
DOI
10.1109/FG.2015.7163113
Filename
7163113
Link To Document