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Gabor Wavelets Transform Based Face Recognition

Posted on:2007-02-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y F LiFull Text:PDF
GTID:1118360182482401Subject:Mechanical design and theory
Abstract/Summary:PDF Full Text Request
Face recognition is a biometric technology possessing great developable potential, researching on the face recognition technology has great theoretical and practical values. In recent years, face recognition technology has achieved unprecedented progress, but its recognition precision in practical applications still cannot satisfy the expectant demands of people, especially under the condition that variations of illumination, photographing azimuth or other disturbance exist in the image. The original face image captured by the recognition system usually is denoted by the grey values of grid pixels. Isolated grey values of pixels cannot reflect the characteristics contained in human face directly, mapping them into the feature space to recognize through adopting appropriate transform is an effective approach to improving the recognition performance. Two-dimensional Gabor wavelets transform can link the pixels in an adjacent region together, and reflect the changes of the grey values of pixels in a local area of an image from different frequency scales and orientations. Two-dimensional Gabor wavelets transform coefficients describe a small patch of grey values in an image around every given position, feature extraction and classification which are based on the two-dimensional Gabor wavelets transform coefficients of face image are called Gabor wavelets transform based face recognition. This dissertation researches into the theory and technology of face recognition through two-dimensional Gabor wavelets transform, the main work and contributions of the dissertation are as follows:(1) The commonly used face recognition theories and methods are summarized compendiously in this dissertation. The research actualities, existing problems and technology development of current face recognition technology are discussed based on a series of international face recognition evaluations which were conducted in recent years.(2) The preprocessing of face image is researched. The aim of face image preprocessing is to regularize the face image which is captured by image collecting devices to normalized image, it includes three steps mainly: face detection and eyes location, geometry normalization, grey value normalization. This dissertation emphasizes on the research of face detection method which is based on AdaBoost statistical learning.(3) Two-dimensional Gabor wavelets transform and its response characteristics in recognition applications are researched. Two-dimensional Gabor wavelets transform is realizedby computing the convolutions of a bank of two-dimensional Gabor filters and the grey values of pixels in an area around a given position in an image. Two-dimensional Gabor wavelets seem to be a good approximation to the receptive fields of the simple cells in the visual cortex of mammalians. In this dissertation, it is validated by the computational results that the local features of face images can be represented through selecting the parameters of Gabor filters, and this kind of representation has the merit of insensitiveness to the absolute brightness of the capturing environment. Recognition based on two-dimensional Gabor wavelets transform surpasses the one based on the grey values of the original image directly.(4) Classical elastic bunch graph matching algorithm is improved. Elastic bunch graph matching algorithm uses labeled graph to represent face image, every node of the labeled graph is labeled with a set of two-dimensional Gabor wavelets transform coefficients which describe the local facial feature, and these nodes lie at the feature point positions of the face image which is useful for recognition;every edge of the labeled graph is labeled with metric information on the relative position of two adjacent nodes, grid structure that is composed by all edges describes the geometrical feature of the whole face. The classical elastic bunch graph matching algorithm matches the face image to a predefined face bunch graph (namely a composite labeled graph, its every node is labeled with a set of Gabor wavelets transform coefficients of corresponding node of many labeled graph, its every edge is labeled with the average of corresponding edge of many labeled graph) in order to obtain the rough positions of the feature points firstly;then, every feature point is located through elastic fine adjustment;lastly, the two-dimensional Gabor wavelets transform coefficients are computed at the feature points and these coefficients are used for face classification and recognition. The computation of classical elastic bunch graph matching algorithm is prohibitive, in this dissertation, seven representative grid structures are obtained through the clustering of the grid structures of the face labeled graphs of many training images, these grid structures are used to constitute a template bunch of the face bunch graph. During the matching stage, the elastic bunch graph matching algorithm is combined with AdaBoost learning algorithm: firstly, the eyes are located by AdaBoost learning algorithm, using eye coordinates as the datum marks, the input image is geometrically normalized;then, the most appropriate grid structure is selected from the template bunch to determine the geometrical feature of a face, and precise matching is performed further based on the outcome. The matching computation is simplified largely after improvements.(5) The influence law of facial features which are represented by two-dimensional Gabor wavelets transform coefficients on face recognition and the corresponding solving methods are researched. This problem is researched from two angles: firstly, the two-dimensional Gaborwavelets transform coefficients at a given facial position are regarded as a local feature cell, the trace of the product of the inversion matrix of the within-class scatter matrix and the between-class scatter matrix is used as a criterion for weighing the discriminative capability of a local feature. By using this criterion, the contributions of the local features that lie at different facial positions to face recognition are analyzed. According to the contributions of different local features to face recognition, face recognition is implemented by the method of local feature weighting;then, a criterion for weighing the discriminative capability of single Gabor feature is proposed. By using this criterion, the discriminative capability of every Gabor wavelets feature of face image is analyzed, and the discriminative capability is linked to its position, frequency and orientation. These analyses can provide the evidences for the optimal selection of the positions of the facial feature points and the parameters of the Gabor wavelets filter bank. According to the discriminative capabilities of Gabor features, one can select the parts that are most advantageous to classification for face recognition.(6) Support Vector Machines classification algorithm is researched, a hierarchical decomposed Support Vector Machines binary decision tree classification scheme is proposed. The pattern set is divided into two parts with similar number of categories at every classification node of the decision tree. During the training stage, as for every classification node, the best partition which divides a pattern set into two parts is found by clustering firstly;then, these two parts are used for Support Vector Machines training. During the recognition stage, the pattern need to be classified is inputted from the root node of the decision tree, and its class label is determined by the leaf node. The classification times of Support Vector Machines in solving multiclass problem can be reduced largely by using this classification scheme.
Keywords/Search Tags:face recognition, Gabor wavelets transform, elastic bunch graph matching, AdaBoost, discriminative capability, feature influence, Support Vector Machines
PDF Full Text Request
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