Study On Facial Expression Recognition Based On Manifold Learning

Facial expression is a basic manner to express human emotion and a powerful way of non-language communication. People can express their thoughts and feelings delicately through expression, and catch others' attitude and inner world by virtue of expression. With the development of face detection, face tracking and face recognition technique, the study of facial expression recognition has become an attractive issue in the areas of pattern recognition and artificial intelligence. At present, the expression recognition technique is still at research phase. Many problems still need to be investigated:how to extract facial expression features exactly, how to reduce the influence of identity features on expression features, how to classify expression vectors accurately, how to define the expression intensity, how to estimate the intensity of expression, and how to analysis mixture expression etc.In view of the non-linearity and continuity of expression variation, some manifold learning algorithms are used to extract facial expression features in this dissertation. The work in this dissertation aims at two issues:one is facial expression recognition; the other is facial expression analysis. The main task of the former is to classify expression images. The aim of the latter is to analyze the intensity of each basic expression component in mixture expression. According to the label information of train samples, two kinds of expression recognition are investigated in this dissertation. One is expression recognition without identity label information; the other is expression recognition with identity label information. The main innovations are as follows:1) A new supervised Iosmap algorithm is proposed in the expression recognition with no identity label. The algorithm reduces the dimension of expression feature vectors and implements the image clustering according to their identity and expression class. This method has cut down the interference between identity features and other expression features.2) For better implementing features classification in expression recognition with no identity label, a sequential weighted k-nearest neighbor classification method is proposed. The method makes good use of the structure of expression manifold for test image classification. It avoids many misclassification situations using weighted k-nearest neighbor classifer and improves the accurate rate of classification.3) A facial expression recognition idea based on face recognition is proposed. First, the face identity is recognized. Then the facial expression is recognized among images with the same identity. A generalized principle component analysis method is proposed countering the identity recognition of ecpression images. The method is an extended algorithm of two-dimensional principal component analysis and modular two-dimensional principal component analysis. The features extraction oprocess will not be restricted by the size of image matrix.4) Aimed at the defection that locally linear embedding is a batch processing algorithm, a locally linear embedding algorithm based on orthogonal iteration is proposed. The method can use the former results of manifold learning continually to compute the manifold vector of new sample. It is an increment manifold embedding algorithm. In the expression recognition with identity label, the method affords many facilities for test images manifold embedding.5) An expression recognition algorithm based on image reconstruction is proposed aimed at a specified subject's expression recognition in expression recognition with the identity label. The method classifies test image according to the similarity between it and its reconstruction images. As expression intensity of reconstruction images will change with test images, the algorithm is robust to expression intensity.6) Many facial expressions in daily life are mixture expression. It does not satisfy the demand of emotion analysis by classifying mixture expression into just one special class of the basic expressions. An expression component analysis method based on manifold space mode is proposed aiming at this demand. With the method, any mixture expression image can be resolved into a vector addition of some of basic expressions in expression manifold space. The method is a novel way to analysis mixture components of facial expression.