@inproceedings{scholars6785,
            year = {2016},
           pages = {3835--3838},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
             doi = {10.1109/EMBC.2016.7591564},
          volume = {2016-O},
            note = {cited By 13; Conference of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference Date: 16 August 2016 Through 20 August 2016; Conference Code:124354},
           title = {Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes},
          author = {Pampouchidou, A. and Marias, K. and Tsiknakis, M. and Simos, P. and Yang, F. and Lemaitre, G. and Meriaudeau, F.},
            issn = {1557170X},
            isbn = {9781457702204},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009089766&doi=10.1109\%2fEMBC.2016.7591564&partnerID=40&md5=dee3ca3ab9f3141c770838fd93b93c9b},
        keywords = {algorithm; computer assisted diagnosis; depression; face; human; procedures; reproducibility; videorecording, Algorithms; Depression; Diagnosis, Computer-Assisted; Face; Humans; Reproducibility of Results; Video Recording},
        abstract = {Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6 classification accuracy, and the person-independed one yielding promising preliminary results of 74.5 accuracy. The paper concludes with open issues, proposed solutions, and future plans. {\^A}{\copyright} 2016 IEEE.}
}