By Topic

Facial expression analysis using 2D and 3D features

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
$31 $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

4 Author(s)
Powar, N.U. ; Sensor Syst. Div., Univ. of Dayton Res. Inst., Dayton, OH, USA ; Foytik, J.D. ; Asari, V.K. ; Vajaria, H.

Psychological research for the recognition of emotions from facial expressions have evolved over the years. Recent technological advances in imaging, computing, computer vision, and pattern recognition have paved the way for automating facial expression recognition. The proposed approach in this paper presents our initial expression classification research using Hidden Markov Models (HMM) on 2D texture facial data. A surface curvature based feature extraction technique involving geometric facial data from unique 3D sensors is also being investigated. It is expected that the proposed methodologies could provide significant improvements in facial expression and emotion recognition performance.

Published in:

Aerospace and Electronics Conference (NAECON), Proceedings of the 2011 IEEE National

Date of Conference:

20-22 July 2011