TY  - CONF
A1  - Pampouchidou, A.
A1  - Simantiraki, O.
A1  - Vazakopoulou, C.-M.
A1  - Chatzaki, C.
A1  - Pediaditis, M.
A1  - Maridaki, A.
A1  - Marias, K.
A1  - Simos, P.
A1  - Yang, F.
A1  - Meriaudeau, F.
A1  - Tsiknakis, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029121274&doi=10.1109%2fEMBC.2017.8037103&partnerID=40&md5=a8c5cb70311a24f926e4bfcda31410d1
EP  - 1436
Y1  - 2017///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 1557170X
N1  - 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
N2  - 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.
TI  - Facial geometry and speech analysis for depression detection
ID  - scholars8390
SP  - 1433
KW  - algorithm; depression; face; human; speech
KW  -  Algorithms; Depression; Depressive Disorder; Face; Humans; Speech
AV  - none
ER  -