%0 Conference Paper
%A Pampouchidou, A.
%A Simantiraki, O.
%A Vazakopoulou, C.-M.
%A Chatzaki, C.
%A Pediaditis, M.
%A Maridaki, A.
%A Marias, K.
%A Simos, P.
%A Yang, F.
%A Meriaudeau, F.
%A Tsiknakis, M.
%D 2017
%F scholars:8390
%I Institute of Electrical and Electronics Engineers Inc.
%K algorithm; depression; face; human; speech, Algorithms; Depression; Depressive Disorder; Face; Humans; Speech
%P 1433-1436
%R 10.1109/EMBC.2017.8037103
%T Facial geometry and speech analysis for depression detection
%U https://khub.utp.edu.my/scholars/8390/
%X Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8 for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation. © 2017 IEEE.
%Z cited By 36; Conference of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference Date: 11 July 2017 Through 15 July 2017; Conference Code:130871