Facial Expression Recognition Based On Visual Feature Extraction

During the past two decades, facial expression recognition has attracted a significant interest in the pattern recognition and artificial intelligence due to its importance for human interfaces. Facial expression recognition plays a vital role in human-computer interaction (HCI). If computer or robot own the understanding and emotional expressing capabilities like human beings, it will thoroughly change the relationship between humankind and computer. And then, computer will provide much better services to human beings. That is also the reasons why we study facial expression recognition and endue computer with emotional understanding and emotional expressing. Many applications, such as emotion analysis, virtual reality, video-conferencing, medical nursing, and customer satisfaction studies for shop and restaurant services and so on, require efficient human expression recognition in order to achieve the desired results. Therefore, it is necessary to develop robust, accurate and fast intellectual automatic facial expression recognition system by analyzing the information of facial expressions. This will also constantly promote applied growth of facial expression recognition.To begin with, the research background of facial expression recognition is introduced, such as the research's origin and developments. What's more, based on the actuality of this research area, we describe a survey of facial expression recognition's methods in terms of feature extraction and expression classification. People distinguish expression easily each other. But, it is a very defiant task for computer because facial expression has relation to age, race, sex, illumination and so forth. And face is a gentle body, rather than just rigid body, and is hard to build up an ideal and precision facial expression model to describe. Feature extraction is an important step of the whole facial expression recognition. The accuracy of feature extraction has an important affection for classification decision. In this paper, we propose feature block principal component analysis (FB-PCA) which fit expression recognition much more by studying the algorithm of principal component analysis. Furthermore, we extend this method to two dimensions and present feature block two dimensional principal component analysis (FB-2DPCA). The main expression information is reserved when the dimension is reduced. We also discuss how to determine the dimension of feature space and draw some conclusions. Otherwise, we propose a method of feature difference matrix (FDM) in order to eliminate the correlation of different expressions. In this way, different expressions can be classified more easily.In the aspect of expression recognition, we propose a new method of facial expression classification, namely, Manhattan distance classifier. Compared with the traditional Euclidean distance classifier and Cosine distance classifier, the method is proposed by us has a great improvement in calculation speed and has some improvement in recognition rate. In order to overcome the problem of indetermination and crossed data lead to low recognition rate, we propose quantum neural network (QNN) classifier. QNN is a kind of neuro-fuzzy system by merging neural modeling with fuzzy-theoretic concepts. That makes network have the fuzzy character and assign indeterminate data to all related categories. Therefore, the QNN decrease indeterminacy of pattern recognition and increase veracity of pattern recognition. Furthermore, we also propose methods of classifier combination and cascade recognition based on the concept of multi-hierarchial to improve recognition rate. But, the wrong classification still gives rise to big risk. In order to solve this problem, a method of self-adaptive threshold regulation is proposed. So threshold can be adjusted by the regulation function.Finally, we summarize our achievements and limitations, and then make an expectation for future research of facial expression recognition at the last part of the thesis.