Cart (Loading....) | Create Account
Close category search window
 

Meta-Analysis of the First Facial Expression Recognition Challenge

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

5 Author(s)
Valstar, M.F. ; Imperial Coll. London, London, UK ; Mehu, M. ; Bihan Jiang ; Pantic, M.
more authors

Automatic facial expression recognition has been an active topic in computer science for over two decades, in particular facial action coding system action unit (AU) detection and classification of a number of discrete emotion states from facial expressive imagery. Standardization and comparability have received some attention; for instance, there exist a number of commonly used facial expression databases. However, lack of a commonly accepted evaluation protocol and, typically, lack of sufficient details needed to reproduce the reported individual results make it difficult to compare systems. This, in turn, hinders the progress of the field. A periodical challenge in facial expression recognition would allow such a comparison on a level playing field. It would provide an insight on how far the field has come and would allow researchers to identify new goals, challenges, and targets. This paper presents a meta-analysis of the first such challenge in automatic recognition of facial expressions, held during the IEEE conference on Face and Gesture Recognition 2011. It details the challenge data, evaluation protocol, and the results attained in two subchallenges: AU detection and classification of facial expression imagery in terms of a number of discrete emotion categories. We also summarize the lessons learned and reflect on the future of the field of facial expression recognition in general and on possible future challenges in particular.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:42 ,  Issue: 4 )
Biometrics Compendium, IEEE

Date of Publication:

Aug. 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.