Research On Facial Feature Points Extraction And Relevant Application

With the development of society and technology, the demand for effective automatic verification is increasingly urgent. Comparing with other human biological characteristics, face characteristics have direct, friendly and convenient characteristics, therefore automatic human face recognition becomes the research focus of identity verification and has extensive application foreground. General speaking, an automatic face recognition system contains: face detection, feature points extraction and recognition. There are some factors which affect instantiation of face recognition. They lie on pose, light and expression of face. Precise facial feature points extraction is the key to solve these problems.In this dissertation, algorithms of facial feature points extraction, tracking and relevant application were profoundly researched. Based on the classical algorithms, novel and creative improvement was proposed. The main discussion of the dissertation is listed as follows:1) The history and status of research on facial feature points extraction are systematically summarized. Detail kinds of feature points extraction approaches, including grey-level-based algorithm, knowledge-based algorithm, Geometry-base algorithm, statistic-based algorithm, wavelet-base algorithm, and then analysis the different algorithm.2) The algorithms of locating rough position of special facial organs are researched. Introduce eyes locating methods including Hough Transform algorithm, Deformable Template algorithm, edge feature analysis algorithm and knowledge-based algorithm. And then introduce Clustering-based mouth locating method.3) Introduce two classical algorithms of facial feature points extraction: Active Shape Model (ASM) and Active Appearance Model (AAM). In this dissertation, we construct ASM and AAM for experiments. With comparing the two algorithms not only in theory but also in experiments, we conclude the weakness and propose the improving solution.4) A method of facial feature points extraction based on improved Active Shape Model and Gabor wavelet features is presented. And the experiments prove that facial feature points can be located robustly and precisely using the method proposed.5) Research the optical flow algorithm and apply it to feature points tracking in serial images of face.6) Explore the method of lip-movement recognition based on track of mouth feature points. An algorithm that extracts the features of lip points' track is proposed. Then introduce subspace learning based dimension reduction algorithm: PCA, LDA, LPP, DCV. And the experiments prove the feasibility of this algorithm proposed.