Facial expression classification: specifying requirements for an automated system

Authors:
Ioanna-Ourania Stathopoulou;George A. Tsihrintzis
Affiliations:
Department of Informatics, University of Piraeus, Piraeus, Greece;Department of Informatics, University of Piraeus, Piraeus, Greece
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

Citing 2
Cited 4

Automatic Classification of Single Facial Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Database for Facial Expression Analysis

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000

Personalised e-learning through an educational virtual reality game using Web services

Multimedia Tools and Applications
NEU-FACES: A Neural Network-Based Face Image Analysis System

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
On Improving Visual-Facial Emotion Recognition with Audio-lingual and Keyboard Stroke Pattern Information

WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
On assisting a visual-facial affect recognition system with keyboard-stroke pattern information

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automated facial expression classification arises as a difficult, yet crucial, pattern recognition problem in the design of human-computer interaction and multimedia interactive service systems. In the process of building NEU-FACES, a novel system of ours for processing multiple camera images of computer user faces to determine their affective state, we conducted and present here an empirical study in which we specify related design requirements, study statistically the expression recognition performance of humans, and identify quantitative facial features of high expression discrimination and classification power.