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Cross-Cultural Detection of Depression from Nonverbal Behaviour

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Authors

Alghowinem, Sharifa
Goecke, Roland
Cohn, Jeffrey F.
Wagner, Michael
Parker, Gordon
Breakspear, Michael

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IEEE Computer Society

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

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Source

The More the Merrier: Analysing the Affect of a Group of People in Images

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Restricted until

2037-12-31