IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Parametric models for facial features segmentation
Signal Processing
Expression recognition using fuzzy spatio-temporal modeling
Pattern Recognition
International Journal of Approximate Reasoning
Effective Emotional Classification Combining Facial Classifiers and User Assessment
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Fuzzy case-based reasoning for facial expression recognition
Fuzzy Sets and Systems
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems
Applied Intelligence
Engineering Applications of Artificial Intelligence
Real-time facial feature point extraction
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Robust facial feature points extraction in color images
Engineering Applications of Artificial Intelligence
A Novel Neuro Fuzzy Approach to Human Emotion Determination
DICTA '10 Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications
Recognition of emotions from video using neural network models
Expert Systems with Applications: An International Journal
Artificial Life and Robotics
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A major issue which divides the facial expressions from the other classification domains is complicated behaviour of human to express the emotions which should be recognised with the classifier model. Existing research recognise the emotions using a range of classification techniques. However, low accuracy rate, large training set, large extracted features or priority for sequence images are the main drawbacks of those works. One of the recent techniques to address the facial expressions problem is fuzzy rule-based system FRBS which is used as a successful method to model and solve the natural-based problems. However, FRBS is poor to adapt the existing knowledge with the diverse conditions. In this article a novel hybrid genetic-fuzzy rule-based model is proposed to optimise the performance of fuzzy classification while the limited raw input data as the features are used. In this model, the proposed genetic algorithm simulates the honey bees offspring generation process called bee royalty offspring algorithm BROA to improve the training process of classic genetic algorithm. The comparison results illustrated that the genetic-fuzzy classification model improves considerably the accuracy rate and performance of FRBS while the BROA modify the training process of genetic-based algorithms.