By Topic

Detecting depression using multimodal approach of emotion recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Imen Tayari Meftah ; INRIA Sophia Antipolis, CNRS and University of Nice Sophia Antipolis Sophia Antipolis, France ; Nhan Le Thanh ; Chokri Ben Amar

Depression is a growing problem in our society. It causes pain and suffering not only to patients but also to those who care about them. This paper presents a multimodal emotion recognition system that is capable of preventing depression. It consists of detecting persistent negative emotions for early detection of depression. Our proposal is based on an algebraic representation of emotional states using multidimensional vectors. This algebraic model provides powerful mathematical tools for the analysis and the processing of emotions and permits the fusion of complementary information such as facial expression, voice, physiological signals, etc. Experiments results show the efficiency of the proposed method in detecting negative emotions by giving high recognition rate.

Published in:

Complex Systems (ICCS), 2012 International Conference on

Date of Conference:

5-6 Nov. 2012