Analysis And Research Of Facial Expression Recognition Algorithm

Facial expression recognition has broad prospects in the field of medicine and civilian applications, nowadays it becomes a hot research field. Facial expressions implicate abundant and exquisite emotional and psychological information. Facial expression recognition is mainly involved with two issues, which are how to obtain the features of facial expression and how to research expression classification.In this dissertation, the main research contents and innovative work include the following five aspects:1) The dissertation provides a comprehensive overview about the history and current situation of facial expression recognition research. It also illustrates three procedures in this field, which are features extraction, facial expression classification and expression recognition framework. Firstly, in the procedure of expression features extraction, the dissertation summarizes some methods of general expression features extraction, and describes some common methods of expression images pretreatment. It analyzes the idea of features extraction based on Gabor wavelet transform, discusses the method of features extraction based on active shape model and active appearance model, and validates the realization of features extraction based on manifold algorithm in this procedure. Secondly, in the procedure of facial expression classification, the dissertation discusses the traditional method based on two classification support vector machine, and illuminates an idea in detail that the expression data can be classified after clustering using cluster algorithm, and then realizes facial expression recognition. It analyzes the algorithm of affinity propagation, and describes the general process of the optimized expression classification center. Thirdly, the general flow framework of expression recognition system is designed in the procedure of expression recognition.2) The dissertation proposes a facial expression recognition algorithm based on Gabor features and congener learning neural network. Firstly, the dissertation researches and analyzes the method of Gabor wavelet features combined with BP neural network, discusses the general model of the BP network structure, and analyzes the project of the decision of hidden layer nodes number, the network transfer function and their application in practice, meanwhile it expatiates the learning process of neural network model aiming at paving the way for congener learning neural network. Secondly, an algorithm of congener learning neural network is proposed in the dissertation, which provides a method to obtain the required features of the neural network input layer, for example, Gabor wavelet is adopted to extract local facial expression features, and the feature region which contributes the most to the facial expression are only selected, then the general concept of congener learning neural network is built. The dissertation illustrates and designs the structure and model of congener learning neural network, it also analyzes the training process of congener learning neural network and describes the implementation of expression recognition. Thirdly, according to the method of local Gabor features and neural network facial expression recognition, some related experiments are tested. The results of experiments provide the distance judgment evidences between two general expressions, and prove the correctness of the method which amends the parameters through the output of inner class expectation and congener expectation distance. In addition, a group of samples are tested for expression classification.3) The dissertation proposes a facial expression recognition algorithm based on ICA features and HMM. Firstly, it introduces the basic ICA model, builds ICA model according to the expression image data, describes the FastICA algorithm procedure, analyzes the process of FastICA algorithm solving separation matrix, utilizes the sliding sub-window to extract training set features, then gets the facial expression basis vector. Secondly, it introduces the PSO algorithm which has outstanding performance of searching optimal solution in the data space. This method avoids the complex computation in extracting expression features. The dissertation shows that the optimized ICA and basic ICA for features extraction have different manifestations. Thirdly, it introduces the general concept of the HMM and the detailed process of HMM expression recognition model, and then it compares and analyzes experiments results based on various parameters of HMM model in order to get the best parameters for classification. It provides the general training process of HMM and the expression recognition system based on the optimized ICA and HMM algorithm. Finally, it compares and analyzes some similar methods and draws a conclusion that some factors will affect the experimental results. Through experimental results, the dissertation verifies that the proposed ideas are correct.4) The dissertation proposes a facial expression recognition algorithm based on AAM features and Adaboost expression classification. Firstly, the ASM method and its general concepts are introduced. Secondly, it provides the evidence according to the marker of expression image and contour, compares the differences between ASM and AAM, and then illustrates the advantages of using AAM to extract facial expression features. Thirdly, it explains Adaboost multi-classification algorithm procedure. In order to apply the features point value of AAM in Adaboost multi-classification, the dissertation constructs the Harr-like features which are quite suitable for Adaboost multi-classification, and through Adaboost classifier it can recognize the facial expression. Finally, through experiments the error rates of training set are analyzed, and the performances are compared in different methods.5) The dissertation proposes a facial expression recognition algorithm based on manifold features and support vector clustering. Firstly, two manifold learning algorithms are introduced, and then an effective and restricted mechanism is proposed according to LPP descending dimension algorithm, meanwhile experiments validate this mechanism can optimize the choice of projection matrix. Secondly, a process of LCSVC clustering is provided which can reduce the interference in clustering marginal of expression classification. In the process of SVC, the method of MFA is adopted to adjust the weight of each SV. Thirdly, a facial expression classifier with four-layer network is designed which is an effective approach to classify expression. At the end, the dissertation validates the parameters in some important procedures and compares the performances in different methods.