@article{scholars12330,
          volume = {10},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
             doi = {10.1109/TAFFC.2017.2724035},
           pages = {445--470},
           title = {Automatic Assessment of Depression Based on Visual Cues: A Systematic Review},
            note = {cited By 83},
            year = {2019},
         journal = {IEEE Transactions on Affective Computing},
          number = {4},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030791568&doi=10.1109\%2fTAFFC.2017.2724035&partnerID=40&md5=e69274be395fb98eb45cc74a912f6bbe},
          author = {Pampouchidou, A. and Simos, P. G. and Marias, K. and Meriaudeau, F. and Yang, F. and Pediaditis, M. and Tsiknakis, M.},
        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},
        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. {\^A}{\copyright} 2010-2012 IEEE.},
            issn = {19493045}
}