TY  - JOUR
VL  - 10
KW  - Artificial intelligence; Flow visualization; Image processing; Learning systems; Monitoring; Reliability; Reliability analysis; Tools
KW  -  Affective Computing; Depression Assessment; Europe; Facial Expressions; Facial images; Mood
KW  -  Learning algorithms
AV  - none
N2  - 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.
Y1  - 2019///
ID  - scholars12330
IS  - 4
N1  - cited By 83
TI  - Automatic Assessment of Depression Based on Visual Cues: A Systematic Review
JF  - IEEE Transactions on Affective Computing
EP  - 470
SP  - 445
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030791568&doi=10.1109%2fTAFFC.2017.2724035&partnerID=40&md5=e69274be395fb98eb45cc74a912f6bbe
A1  - Pampouchidou, A.
A1  - Simos, P.G.
A1  - Marias, K.
A1  - Meriaudeau, F.
A1  - Yang, F.
A1  - Pediaditis, M.
A1  - Tsiknakis, M.
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 19493045
ER  -