TY - GEN
T1 - SNIP
T2 - 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015
AU - Nurzynska, K.
AU - Smolka, B.
N1 - Publisher Copyright:
© 2016 Taylor & Francis Group, London.
PY - 2016
Y1 - 2016
N2 - Many affective disorders can influence the capacity of emotion expression and understanding. For instance, in the case of depression, the frequency and intensity of smiling is diminishing when the disease proceeds. Analysing these changes by a psychologist could bring information about the effectiveness of the prescribed medical treatment. Therefore, this work describes a system which automatically analyses video recordings from sessions with patients and determines the length and intensity of smile. The proposed system exploits a standard database to train the classifier, which is later used to distinguish between the smiling and neutral facial expression depicted in the video frames. The obtained information is presented graphically, indicating the class membership and temporal smile intensity. The performed experiments revealed that the classification accuracy exceeds 85%. Moreover, the case studies show, that the variations in smile magnitude enable better insight into the smile analysis.
AB - Many affective disorders can influence the capacity of emotion expression and understanding. For instance, in the case of depression, the frequency and intensity of smiling is diminishing when the disease proceeds. Analysing these changes by a psychologist could bring information about the effectiveness of the prescribed medical treatment. Therefore, this work describes a system which automatically analyses video recordings from sessions with patients and determines the length and intensity of smile. The proposed system exploits a standard database to train the classifier, which is later used to distinguish between the smiling and neutral facial expression depicted in the video frames. The obtained information is presented graphically, indicating the class membership and temporal smile intensity. The performed experiments revealed that the classification accuracy exceeds 85%. Moreover, the case studies show, that the variations in smile magnitude enable better insight into the smile analysis.
UR - https://www.scopus.com/pages/publications/84959263596
M3 - Conference contribution
AN - SCOPUS:84959263596
SN - 9781138029262
T3 - Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015
SP - 347
EP - 354
BT - Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015
A2 - Tavares, Joao Manuel R.S.
A2 - Jorge, R.M. Natal
PB - CRC Press/Balkema
Y2 - 19 October 2015 through 21 October 2015
ER -