eprintid: 6785
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/67/85
datestamp: 2023-11-09 16:18:35
lastmod: 2023-11-09 16:18:35
status_changed: 2023-11-09 16:07:39
type: conference_item
metadata_visibility: show
creators_name: Pampouchidou, A.
creators_name: Marias, K.
creators_name: Tsiknakis, M.
creators_name: Simos, P.
creators_name: Yang, F.
creators_name: Lemaitre, G.
creators_name: Meriaudeau, F.
title: Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes
ispublished: pub
keywords: algorithm; computer assisted diagnosis; depression; face; human; procedures; reproducibility; videorecording, Algorithms; Depression; Diagnosis, Computer-Assisted; Face; Humans; Reproducibility of Results; Video Recording
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
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. © 2016 IEEE.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009089766&doi=10.1109%2fEMBC.2016.7591564&partnerID=40&md5=dee3ca3ab9f3141c770838fd93b93c9b
id_number: 10.1109/EMBC.2016.7591564
full_text_status: none
publication: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
volume: 2016-O
pagerange: 3835-3838
refereed: TRUE
isbn: 9781457702204
issn: 1557170X
citation:   Pampouchidou, A. and Marias, K. and Tsiknakis, M. and Simos, P. and Yang, F. and Lemaitre, G. and Meriaudeau, F.  (2016) Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.  In: UNSPECIFIED.