Emotion recognition through facial expression analysis based on a neurofuzzy network.
Extracting and validating emotional cues through analysis of users' facial expressions is of high importance for improving the level of interaction in man machine communication systems. Extraction of appropriate facial features and consequent recognition of the user's emotional state that can be robust to facial expression variations among different users is the topic of this paper. Facial animation parameters (FAPs) defined according to the ISO MPEG-4 standard are extracted by a robust facial analysis system, accompanied by appropriate confidence measures of the estimation accuracy. A novel neurofuzzy system is then created, based on rules that have been defined through analysis of FAP variations both at the discrete emotional space, as well as in the 2D continuous activation-evaluation one. The neurofuzzy system allows for further learning and adaptation to specific users' facial expression characteristics, measured though FAP estimation in real life application of the system, using analysis by clustering of the obtained FAP values. Experimental studies with emotionally expressive datasets, generated in the EC IST ERMIS project indicate the good performance and potential of the developed technologies.
Ioannou
Spiros V
SV
Image, Video and Multimedia Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Greece.
Raouzaiou
Amaryllis T
AT
Tzouvaras
Vasilis A
VA
Mailis
Theofilos P
TP
Karpouzis
Kostas C
KC
Kollias
Stefanos D
SD
D000223:Adaptation, Psychological; D019992:Databases as Topic; D004644:Emotions / Q000502:physiology*; D005149:Facial Expression; D006801:Humans; D007858:Learning; D008328:Man-Machine Systems; D016571:Neural Networks, Computer; D021641:Recognition, Psychology / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology