期刊
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 10, 期 4, 页码 445-470出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2017.2724035
关键词
Visualization; Affective computing; Monitoring; Europe; Mood; Reliability; Tools; Depression assessment; affective computing; facial expression; machine learning; facial image analysis
资金
- Greek State Scholarship Foundation
- grant entitled SEMEOTICONS (SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self-monitoring) - European Commission [611516]
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.
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