1. Automatic Assessment of Depression Based on Visual Cues: A Systematic Review
- Author
-
Kostas Marias, Fan Yang, Manolis Tsiknakis, Anastasia Pampouchidou, Panagiotis G. Simos, Matthew Pediaditis, Fabrice Meriaudeau, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), University of Crete School of medicine, Institute of Computer Science ( ICS-FORTH ), Foundation for Research and Technology - Hellas ( FORTH ), SRI International [Menlo Park] ( SRI ), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Institute of Computer Science (ICS-FORTH), Foundation for Research and Technology - Hellas (FORTH), Department of Informatics Engineering, School of Engineering [TEI of Crete], Technological Educational Institute of Crete, Centre for Intelligent Signal and Imaging Research [Petronas] (CISIR), Universiti Teknologi PETRONAS (UTP), Greek Ministry of Development-GSRT, European Project: 611516,SEMEOTICONS, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Institute of Computer Science [FORTH, Heraklion] (ICS-FORTH), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement
- Subjects
Monitoring ,Rating-Scale ,Remission ,Computer science ,Performance ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Adolescents ,computer.software_genre ,Tools ,Attentional Bias ,[SPI]Engineering Sciences [physics] ,03 medical and health sciences ,0302 clinical medicine ,Dynamic-Analysis ,Mood ,Diagnosis ,Disorder ,[ SPI ] Engineering Sciences [physics] ,0202 electrical engineering, electronic engineering, information engineering ,affective computing ,Affective computing ,Sensory cue ,ComputingMilieux_MISCELLANEOUS ,Visualization ,Facial expression ,Data collection ,Contextual image classification ,business.industry ,Dimensionality reduction ,facial image analysis ,Reliability ,Europe ,Facial Expression ,Human-Computer Interaction ,machine learning ,Depression assessment ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery ,Software ,Natural language processing - Abstract
International audience; Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.Visualization; Affective computing; Monitoring; Europe; Mood; Reliability; Tools; Depression assessment; affective computing; facial expression; machine learning; facial image analysis
- Published
- 2019
- Full Text
- View/download PDF