Stablization of an inverted pendulum by a high-speed fuzzy logic controller hardware system
Fuzzy Sets and Systems - On Applications of Fuzzy Logic Control to Industry
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An Expert System for Multiple Emotional Classification of Facial Expressions
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Fuzzy Rule Based Facial Expression Recognition
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Near-Optimal fuzzy systems using polar clustering: application to control of vision-based arm-robot
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Engineering Applications of Artificial Intelligence
Facial expression recognition in dynamic sequences: An integrated approach
Pattern Recognition
Hi-index | 0.00 |
In this paper we present a fuzzy reasoning system that can measure and recognize the intensity of basic or non-prototypical facial expressions. The system inputs are the encoded facial deformations described either in terms of Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard. The proposed fuzzy system uses a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows the modeling of facial features obtained from geometric parameters coded by AUs - FAPs and also the definition of rules required for classification of measured expressions. This paper also presents the designed framework for fuzzyfication of input variables for fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanade's and Pantic's MMI face databases. The proposed system has been tested in order to evaluate its capability for detection, classifying, and interpretation of facial expressions.