Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9730
Title: Automatic depression scale prediction using facial expression dynamics and regression
Authors: Jan, A
Meng, H
Gaus, YFA
Zhang, F
Turabzadeh, S
Keywords: Affective computing;Depression recognition;Beck depression;Inventory;Facial expression;Challenge
Issue Date: 2014
Publisher: ACM
Citation: Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (AVEC14): 73 - 80, (2014)
Abstract: Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behaviour, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.
URI: http://dl.acm.org/citation.cfm?doid=2661806.2661812
http://bura.brunel.ac.uk/handle/2438/9730
DOI: http://dx.doi.org/10.1145/2661806.2661812
ISBN: 978-1-4503-3119-7
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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