By Topic

Facial expression recognition from image sequences using optimized feature selection

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

2 Author(s)
Lajevardi, S.M. ; Sch. of Electr.&Comput. Eng., RMIT Univ., Melbourne, VIC ; Lech, M.

A novel method for facial expression recognition from sequences of image frames is described and tested. The expression recognition system is fully automatic, and consists of the following modules: face detection, maximum arousal detection, feature extraction, selection of optimal features, and facial expression recognition. The face detection is based on AdaBoost algorithm and is followed by the extraction of frames with the maximum arousal (intensity) of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features based on the log-Gabor filter method combined with an optimal feature selection process, which uses the MIFS algorithm. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features were classified using the Naive Bayesian (NB) classifier.The system was tested using image sequences from the Cohn-Kanade database. The percentage of correct classification was increased from 68.9% for the non-optimized features to 79.5% for the optimized set of features.

Published in:

Image and Vision Computing New Zealand, 2008. IVCNZ 2008. 23rd International Conference

Date of Conference:

26-28 Nov. 2008