Fuzzy neural networks(FNN)-based approach for personalized facial expression recognition with novel feature selection method

Facial expression recognition is very important in many human-robot/human-computer interaction systems. Although so many researches are done, it is hard to find a practical applications in the real world due to its underestimate about individual differences among people. Thus, as a solution for such problem, we introduce a 'personalized' facial expression recognition system. Many previous works on facial expression recognition focus on the well-known six universal facial expressions (happy, sad, fear, angry, surprise and disgust) under usage of unified (or non-separated) classification approach. However, for ordinary people, it is a very difficult task to make such facial expressions without much effort and training. Instead of universal facial expressions, many people show 'personalized' or 'individualized' facial expressions typically. Thus, for dealing with such personalities, we propose a method to construct a personalized classifier based on novel feature selection method. Specifically, feature selection is done by histogram-based approach in the frame of fuzzy neural networks(FNN). Besides, we also use an integrated approach for facial expression recognition. Actual experiments/simulations show that the proposed method is effective not only in view of facial expression recognition but also in view of pattern classifier itself.