A Video-Based Facial Behaviour Analysis Approach to Melancholia

Recent years have seen a lot of activity in affective computing for automated analysis of depression. However, no research has so far directly evaluated the performance of facial behavioural analysis methods in classifying different subtypes of depression such as melancholia. The mental state assessment of a mood disorder depends largely on appearance, behaviour, speech, thought, perception, mood and facial affect. Mood and facial affect mainly contribute to distinguishing melancholia from nonmelancholia. These are assessed by clinicians, and hence vulnerable to subjective judgement. As a result, clinical assessment alone may not accurately capture the presence or absence of specific disorders such as melancholia, a distressing condition whose presence has important treatment implications. Melancholia is characterised by severe anhedonia and psychomotor disturbance, which can be a mix of motor retardation with periods of superimposed agitation. To the best of our knowledge, this study is the first attempt to perform facial behavioural analysis to disambiguate melancholia from non-melancholia and healthy controls on the basis of facial behavioural characteristics. We report the sensitivity and specificity of classification in depressive subtypes. These results serve as a baseline for more fine-grained depression classification and analysis.

[1]  G. Parker,et al.  Classifying depression: should paradigms lost be regained? , 2000, The American journal of psychiatry.

[2]  Karl J. Friston,et al.  Disrupted effective connectivity of cortical systems supporting attention and interoception in melancholia. , 2015, JAMA psychiatry.

[3]  Tamás D. Gedeon,et al.  Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015 , 2015, ICMI.

[4]  Fernando De la Torre,et al.  Detecting depression from facial actions and vocal prosody , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[5]  Roland Göcke,et al.  An approach for automatically measuring facial activity in depressed subjects , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[6]  Jeffrey M Girard,et al.  Automated Audiovisual Depression Analysis. , 2015, Current opinion in psychology.

[7]  L. Kessing,et al.  Epidemiology of subtypes of depression , 2007, Acta psychiatrica Scandinavica. Supplementum.

[8]  Melancholia: Beyond DSM, Beyond Neurotransmitters. Proceedings of a conference, May 2006, Copenhagen, Denmark. , 2007, Acta psychiatrica Scandinavica. Supplementum.

[9]  Michael Wagner,et al.  Multimodal assistive technologies for depression diagnosis and monitoring , 2013, Journal on Multimodal User Interfaces.

[10]  J De Courcy Dealing with depression , 1990, The Lancet.

[11]  Robert Michels,et al.  Issues for DSM-5: whither melancholia? The case for its classification as a distinct mood disorder. , 2010, The American journal of psychiatry.

[12]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  P. Mitchell,et al.  Subtyping depression by clinical features:the Australasian database , 2000, Acta psychiatrica Scandinavica.

[14]  Ivan Laptev,et al.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

[15]  Gordon Parker,et al.  Melancholia: definition and management , 2014, Current opinion in psychiatry.

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  Fabien Ringeval,et al.  AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge , 2016, AVEC@ACM Multimedia.

[18]  Mohammed Bennamoun,et al.  Evaluation of Spatiotemporal Detectors and Descriptors for Facial Expression Recognition , 2012, 2012 5th International Conference on Human System Interactions.

[19]  G. Parker,et al.  Measuring melancholia: the utility of a prototypic symptom approach , 2008, Psychological Medicine.

[20]  Mohammed Bennamoun,et al.  An Automatic Framework for Textured 3D Video-Based Facial Expression Recognition , 2014, IEEE Transactions on Affective Computing.

[21]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Edward Shorter,et al.  Melancholia: restoration in psychiatric classification recommended , 2007, Acta psychiatrica Scandinavica.

[24]  J. Markowitz,et al.  The 16-Item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression , 2003, Biological Psychiatry.

[25]  Hamdi Dibeklioglu,et al.  Multimodal Detection of Depression in Clinical Interviews , 2015, ICMI.

[26]  Gordon Parker,et al.  The properties and utility of the CORE measure of melancholia. , 2017, Journal of affective disorders.

[27]  Gordon Parker,et al.  Atypical depression: Australian and US studies in accord ‐ special commentary , 2005, Current opinion in psychiatry.

[28]  A. Aboraya,et al.  The Reliability of Psychiatric Diagnosis Revisited: The Clinician's Guide to Improve the Reliability of Psychiatric Diagnosis. , 2006, Psychiatry (Edgmont (Pa. : Township)).

[29]  Szymon Fedor Can We Predict Depression From the Asymmetry of Electrodermal Activity , 2016 .

[30]  Peter Robinson,et al.  OpenFace: An open source facial behavior analysis toolkit , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[31]  P. Mitchell,et al.  The nature of bipolar depression: implications for the definition of melancholia. , 2000, Journal of affective disorders.

[32]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.