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The Research On Facial Expression Recognition For Intelligent Service Robot

Posted on:2016-12-01Degree:DoctorType:Dissertation
Country:ChinaCandidate:L WangFull Text:PDF
GTID:1108330503969587Subject:Mechanical and electrical engineering
Abstract/Summary:PDF Full Text Request
Intelligent service robot, which has a broad application prospect in teaching, entertainment, tour guiding, elderly or disabled people assistance and other fields, is a very popular and significant research field today. Generally, the ability of natural and harmonious human-computer interaction(HCI) is necessary for intelligent service robot. Since containing rich delicate information of emotional change and mental activity, human facial expression plays an irreplaceable role in social communication. Facial expression recognition(FER) and anthropomorphic robot head are two important parts of the intelligent service robot system, and the former is for the input of operator’s expression and the latter for the output of robot’s expression. It would make much sense in theoretical research and practical application to implement natural and efficient HCI through research and development of FER algorithm and robot head entity.In order to realize natural and harmonious HCI, we conduct the following research work by centering on FER and mechanical structure of humanoid robot head. Firstly, the automatic face region segmentation method of real time, compound local binary patterns(C-LBP), unsupervised learning of LBP feature selection and expression classifier design of matrix spectral regression analysis(MSRA) are studied in the process of FER, which provide recognition algorithms basis for intelligent HCI. Finally, the experimental system construction of expression representation for humanoid head robot is furtherly developed based on them and their effectivenesses are verified by experiments. The content in detailed is as follows.Face detection is the most important and necessary step of FER, which mainly provides accurate face region for object. The active shape model(ASM) is used to build models for face recognition according to the contour of face features, which reduce the influence of head pose change, remove the area outside face to improve rates of detection and extract face region. The landmarks on faces are applied to extract the key areas based on facial action coding system(FACS), and locate the face position and the feature region for FER exactly.It is great significant for FER to extract effective facial features. By research on “uniform” local binary patterns(LBP) form local feature operator, C-LBP operator is proposed by describing the tendency and extent of facial texture changes. It reveals the changes of data structure from multiple neighborhoods and directions to extract features in the main facial expression regions, which provides recognition vectors for FER.It is an effective technique that the recognition effectiveness can be improved by reducing the redundant information. Though LBP can extract the local texture features of images effectively, its dimensionality harshly grows companied with the number of pixels in the neighborhood region. Hence, the neighborhood graph in the feature space is firstly built by applying chi square statistic according to the histogram of LBP property in this paper. Then, the new feature subset is formed for FER by selecting more representative features according to 2LS- c scores calculated by applying binary weight matrix.Classifier design is the most important step of FER. Linear discriminant analysis(LDA), as a traditional statistical classification algorithm, cannot solve the nonlinear problem in the feature space effectively. Its computation amount is tremendous large for a great number of samples with high dimensionality in addition to the obsession from the singular values in the process of solving eigenvalues. Based on the theory of spectral graph and kernel learning, expression classifier MSRA is developed for multiple expression recognition after analyzing the relationship between linear coefficient in the kernel space and weighted matrix from LDA. It avoids calculating eigenvalues in the process of training classifier and reduces computation time and memory.It is very important that the intelligent service robot can perform facial expression for realization HCI of intelligence, emotion and humanlike. From bionics, the expression representation system of intelligent robot is constructed based on humanoid head robot and FER technique. Experiments of multiple movements on head mechanisms of this system and validations of FER algorithms in real situation are performed. Expression representation is realized by using expression class in real scene.In this paper, experiments for related FER techniques on databases and real situation are executed. The effectiveness and availability are validated on the proposed C-LBP operator, unsupervised feature selection algorithm for LBP feature and expression classifier MSRA. And the real-time and robustness of automatic face region extraction in real situation is validated. The well recognition effectiveness is obtained on databases and in real situation, and the real-time automatic extraction of face region in real situation is realized. Expression representation on humanoid head robot is achieved by FER results.
Keywords/Search Tags:robot interaction, feature extraction, feature selection, facial expression recognition, expression representation
PDF Full Text Request
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