eprintid: 12330
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/23/30
datestamp: 2023-11-10 03:26:52
lastmod: 2023-11-10 03:26:52
status_changed: 2023-11-10 01:17:25
type: article
metadata_visibility: show
creators_name: Pampouchidou, A.
creators_name: Simos, P.G.
creators_name: Marias, K.
creators_name: Meriaudeau, F.
creators_name: Yang, F.
creators_name: Pediaditis, M.
creators_name: Tsiknakis, M.
title: Automatic Assessment of Depression Based on Visual Cues: A Systematic Review
ispublished: pub
keywords: Artificial intelligence; Flow visualization; Image processing; Learning systems; Monitoring; Reliability; Reliability analysis; Tools, Affective Computing; Depression Assessment; Europe; Facial Expressions; Facial images; Mood, Learning algorithms
note: cited By 83
abstract: 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. © 2010-2012 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030791568&doi=10.1109%2fTAFFC.2017.2724035&partnerID=40&md5=e69274be395fb98eb45cc74a912f6bbe
id_number: 10.1109/TAFFC.2017.2724035
full_text_status: none
publication: IEEE Transactions on Affective Computing
volume: 10
number: 4
pagerange: 445-470
refereed: TRUE
issn: 19493045
citation:   Pampouchidou, A. and Simos, P.G. and Marias, K. and Meriaudeau, F. and Yang, F. and Pediaditis, M. and Tsiknakis, M.  (2019) Automatic Assessment of Depression Based on Visual Cues: A Systematic Review.  IEEE Transactions on Affective Computing, 10 (4).  pp. 445-470.  ISSN 19493045