Authors:
Ioanna-Ourania Stathopoulou
and
George A. Tsihrintzis
Affiliation:
University of Piraeus, Greece
Keyword(s):
Facial Expression Classification, Human Emotion, Knowledge Representation, Human-Computer Interaction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Acquisition
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Automated facial expression classification is very important in the design of new human-computer interaction modes and multimedia interactive services and arises as a difficult, yet crucial, pattern recognition problem. Recently, we have been building such a system, called NEU-FACES, which processes multiple camera images of computer user faces with the ultimate goal of determining their affective state. In here, we present results from an empirical study we conducted on how humans classify facial expressions, corresponding error rates, and to which degree a face image can provide emotion recognition from the perspective of a human observer. This study lays related system design requirements, quantifies statistical expression recognition performance of humans, and identifies quantitative facial features of high expression discrimination and classification power.