eigenfaces vs fisherfaces recognition using class specific linear projection we develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression taking a pattern classification approach we consider each pixel in an image as a coordinate in a high dimensional space we take advantage of the observation that the images of a particular face under varying illumination but fixed pose lie in a 3d linear subspace of the high dimensional image space if the face is a lambertian surface without shadowing however since faces are not truly lambertian surfaces and do indeed produce self shadowing images will deviate from this linear subspace rather than explicitly modeling this deviation we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation our projection method is based on fisher s linear discriminant and produces well separated classes in a low dimensional subspace even under severe variation in lighting and facial expressions the eigenface technique another method based on linearly projecting the image space to a low dimensional subspace has similar computational requirements yet extensive experimental results demonstrate that the proposed fisherface method has error rates that are lower than those of the eigenface technique for tests on the harvard and yale face databases

gabor based kernel pca with fractional power polynomial models for face recognition this paper presents a novel gabor based kernel principal component analysis pca method by integrating the gabor wavelet representation of face images and the kernel pca method for face recognition gabor wavelets first derive desirable facial features characterized by spatial frequency spatial locality and orientation selectivity to cope with the variations due to illumination and facial expression changes the kernel pca method is then extended to include fractional power polynomial models for enhanced face recognition performance a fractional power polynomial however does not necessarily define a kernel function as it might not define a positive semidefinite gram matrix note that the sigmoid kernels one of the three classes of widely used kernel functions polynomial kernels gaussian kernels and sigmoid kernels do not actually define a positive semidefinite gram matrix either nevertheless the sigmoid kernels have been successfully used in practice such as in building support vector machines in order to derive real kernel pca features we apply only those kernel pca eigenvectors that are associated with positive eigenvalues the feasibility of the gabor based kernel pca method with fractional power polynomial models has been successfully tested on both frontal and pose angled face recognition using two data sets from the feret database and the cmu pie database respectively the feret data set contains 600 frontal face images of 200 subjects while the pie data set consists of 680 images across five poses left and right profiles left and right half profiles and frontal view with two different facial expressions neutral and smiling of 68 subjects the effectiveness of the gabor based kernel pca method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the pca method the kernel pca method with polynomial kernels the kernel pca method with fractional power polynomial models the gabor wavelet based pca method and the gabor wavelet based kernel pca method with polynomial kernels

phantom faces for face analysis the system presented is part of a general object recognition system images of faces are represented as graphs labeled with topographical information and local features new graphs of faces are generated by an elastic graph matching procedure comparing the new face with a composition of stored graphs the face bunch graph the result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the bunch graph such as sex or the presence of glasses or a beard

using support vector machines to enhance the performance of elastic graph matching for frontal face authentication a novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed the starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power powerful and well established optimization techniques are used to derive the weights of the linear combination more specifically we propose a novel approach that reformulates fisher s discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines svm both linear and nonlinear svm are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces respectively the method has been applied to frontal face authentication on the m2vts database experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique

exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification in this paper two supervised methods for enhancing the classification accuracy of the nonnegative matrix factorization nmf algorithm are presented the idea is to extend the nmf algorithm in order to extract features that enforce not only the spatial locality but also the separability between classes in a discriminant manner the first method employs discriminant analysis in the features derived from nmf in this way a two phase discriminant feature extraction procedure is implemented namely nmf plus linear discriminant analysis lda the second method incorporates the discriminant constraints inside the nmf decomposition thus a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived the introduced methods have been applied to the problem of frontal face verification using the well known xm2vts database both methods greatly enhance the performance of nmf for frontal face verification

generalized q analysis of log periodicity applications to critical ruptures we introduce a generalization of the q analysis which provides a nonparametric tool for the description and detection of log periodic structures associated with discrete scale invariance we use this generalized q analysis to construct a signature called the h q derivative of discrete scale invariance which we use to detect the log periodicity in the cumulative energy release preceding the rupture of five pressure tanks made of composite carbon matrix material we investigate the significance level of the spectral lomb periodogram of the optimal h q derivative we confirm and strengthen previous parametric results that the cumulative energy release exhibits log periodicity before rupture however our tests to use this method as a scheme for the prediction of the critical value of the stress at rupture are not encouraging

alternative linear discriminant classifier abstract fisher linear discriminant analysis flda finds a set of optimal discriminating vectors by maximizing fisher criterion i e the ratio of the between scatter to the within scatter one of its major disadvantages is that the number of its discriminating vectors capable to be found is bounded from above by c 1 for c class problem in this paper for binary class problem we propose alternative flda to breakthrough this limitation by only replacing the original between scatter with a new scatter measure the experimental results show that our approach give impressive recognition performances compared to both the fisher approach and linear svm

morphological elastic graph matching applied to frontal face authentication under well controlled and real conditions abstract in this paper morphological elastic graph matching is applied to frontal face authentication on databases ranging from small to large multimedia ones collected under either well controlled or real world conditions it is shown that the morphological elastic graph matching achieves a very low equal error rate on databases collected under well controlled conditions however its performance deteriorates when it is applied to databases recorded in real world scenarios the compensation for variable recording conditions such as changes in illumination scale differences and varying face position prior to the application of morphological elastic matching is proposed

object recognition robust under translations deformations and changes in background recognition systems based on model matching using low level features often fail due to a variation in background as a solution i present a system for the recognition of human faces independent of hairstyle correspondence maps between an image and a model are established by coarse fine matching in a gabor pyramid these are used for hierarchical recognition

face authentication with gabor information on deformable graphs elastic graph matching has been proposed as a practical implementation of dynamic link matching which is a neural network with dynamically evolving links between a reference model and an input image each node of the graph contains features that characterize the neighborhood of its location in the image the elastic graph matching usually consists of two consecutive steps namely a matching with a rigid grid followed by a deformation of the grid which is actually the elastic part the deformation step is introduced in order to allow for some deformation rotation and scaling of the object to be matched this method is applied here to the authentication of human faces where candidates claim an identity that is to be checked the matching error as originally suggested is not powerful enough to provide satisfying results in this case we introduce an automatic weighting of the nodes according to their significance we also explore the significance of the elastic deformation for an application of face based person authentication we compare performance results obtained with and without the second matching step results show that the deformation step slightly increases the performance but has lower influence than the weighting of the nodes the best results are obtained with the combination of both aspects the results provided by the proposed method compare favorably with two methods that require a prior geometric face normalization namely the synergetic and eigenface approaches

the discriminant elastic graph matching algorithm applied to frontal face verification in this paper a generalized framework for face verification is proposed employing discriminant techniques in all phases of elastic graph matching the proposed algorithm is called discriminant elastic graph matching degm algorithm in the first step of the proposed method degm discriminant techniques at the node feature vectors are used for feature selection in the sequel the two local similarity values i e the similarity measure for the projected node feature vector and the node deformation are combined in a discriminant manner in order to form the new local similarity measure moreover the new local similarity values at the nodes of the elastic graph are weighted by coefficients that are derived as well from discriminant analysis in order to form a total similarity measure between faces the proposed method exploits the individuality of the human face and the discriminant information of elastic graph matching in order to improve the verification performance of elastic graph matching we have applied the proposed scheme to a modified morphological elastic graph matching algorithm all experiments have been conducted in the xm2vts database resulting in very low error rates for the test sets

independent component analysis of gabor features for face recognition we present an independent gabor features igfs method and its application to face recognition the novelty of the igf method comes from 1 the derivation of independent gabor features in the feature extraction stage and 2 the development of an igf features based probabilistic reasoning model prm classification method in the pattern recognition stage in particular the igf method first derives a gabor feature vector from a set of downsampled gabor wavelet representations of face images then reduces the dimensionality of the vector by means of principal component analysis and finally defines the independent gabor features based on the independent component analysis ica the independence property of these gabor features facilitates the application of the prm method for classification the rationale behind integrating the gabor wavelets and the ica is twofold on the one hand the gabor transformed face images exhibit strong characteristics of spatial locality scale and orientation selectivity these images can thus produce salient local features that are most suitable for face recognition on the other hand ica would further reduce redundancy and represent independent features explicitly these independent features are most useful for subsequent pattern discrimination and associative recall experiments on face recognition using the face recognition technology feret and the orl datasets where the images vary in illumination expression pose and scale show the feasibility of the igf method in particular the igf method achieves 98 5 correct face recognition accuracy when using 180 features for the feret dataset and 100 accuracy for the orl dataset using 88 features

multisoliton perturbation theory for the benjamin ono equation and its application to real physical systems a direct perturbation theory is developed to study the effects of small perturbations on the interaction process of algebraic solitons of the benjamin ono bo equation using the method of multiple scales the modulation equations for the amplitude and the phase of each soliton are derived in the lowest approximation as practical applications of the theory the interaction of two solitons is investigated for the two different types of perturbations that appear in real physical systems one is a dissipative perturbation bo burgers equation and the other is a dispersive perturbation higher order bo equation in both cases the changes of the soliton parameters due to small perturbation are calculated by numerical integrations and their characteristics are elucidated in detail among them the phase shift caused by the dispersive perturbation is a remarkable feature that has never been observed in the collision process of algebraic solitons

an algorithm for the learning of weights in discrimination functions using a priori constraints we introduce a learning algorithm for the weights in a very common class of discrimination functions usually called weighted average the learning algorithm can reduce the number of free variables by simple but effective a priori criteria about significant features here we apply our algorithm to three tasks of different dimensionality all concerned with face recognition

face recognition using kernel direct discriminant analysis algorithms techniques that can introduce low dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition fr systems it is well known that the distribution of face images under a perceivable variation in viewpoint illumination or facial expression is highly nonlinear and complex it is therefore not surprising that linear techniques such as those based on principle component analysis pca or linear discriminant analysis lda cannot provide reliable and robust solutions to those fr problems with complex face variations in this paper we propose a kernel machine based discriminant analysis method which deals with the nonlinearity of the face patterns distribution the proposed method also effectively solves the so called small sample size sss problem which exists in most fr tasks the new algorithm has been tested in terms of classification error rate performance on the multiview umist face database results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used and its error rate is approximately 34 and 48 of those of two other commonly used kernel fr approaches the kernel pca kpca and the generalized discriminant analysis gda respectively

kpca plus lda a complete kernel fisher discriminant framework for feature extraction and recognition this paper examines the theory of kernel fisher discriminant analysis kfd in a hilbert space and develops a two phase kfd framework i e kernel principal component analysis kpca plus fisher linear discriminant analysis lda this framework provides novel insights into the nature of kfd based on this framework the authors propose a complete kernel fisher discriminant analysis ckfd algorithm ckfd can be used to carry out discriminant analysis in double discriminant subspaces the fact that it can make full use of two kinds of discriminant information regular and irregular makes ckfd a more powerful discriminator the proposed algorithm was tested and evaluated using the feret face database and the cenparmi handwritten numeral database the experimental results show that ckfd outperforms other kfd algorithms

a shape and texture based enhanced fisher classifier for face recognition this paper introduces a new face coding and recognition method the enhanced fisher classifier efc which employs the enhanced fisher linear discriminant model efm on integrated shape and texture features shape encodes the feature geometry of a face while texture provides a normalized shape free image the dimensionalities of the shape and the texture spaces are first reduced using principal component analysis constrained by the efm for enhanced generalization the corresponding reduced shape and texture features are then combined through a normalization procedure to form the integrated features that are processed by the efm for face recognition experimental results using 600 face images corresponding to 200 subjects of varying illumination and facial expressions show that 1 the integrated shape and texture features carry the most discriminating information followed in order by textures masked images and shape images and 2 the new coding and face recognition method efc performs the best among the eigenfaces method using l sub 1 or l sub 2 distance measure and the mahalanobis distance classifiers using a common covariance matrix for all classes or a pooled within class covariance matrix in particular efc achieves 98 5 recognition accuracy using only 25 features

an introduction to kernel based learning algorithms this paper provides an introduction to support vector machines kernel fisher discriminant analysis and kernel principal component analysis as examples for successful kernel based learning methods we first give a short background about vapnik chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations we illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and dna analysis

gabor feature based classification using the enhanced fisher linear discriminant model for face recognition this paper introduces a novel gabor fisher 1936 classifier gfc for face recognition the gfc method which is robust to changes in illumination and facial expression applies the enhanced fisher linear discriminant model efm to an augmented gabor feature vector derived from the gabor wavelet representation of face images the novelty of this paper comes from 1 the derivation of an augmented gabor feature vector whose dimensionality is further reduced using the efm by considering both data compression and recognition generalization performance 2 the development of a gabor fisher classifier for multi class problems and 3 extensive performance evaluation studies in particular we performed comparative studies of different similarity measures applied to various classifiers we also performed comparative experimental studies of various face recognition schemes including our novel gfc method the gabor wavelet method the eigenfaces method the fisherfaces method the efm method the combination of gabor and the eigenfaces method and the combination of gabor and the fisherfaces method the feasibility of the new gfc method has been successfully tested on face recognition using 600 feret frontal face images corresponding to 200 subjects which were acquired under variable illumination and facial expressions the novel gfc method achieves 100 accuracy on face recognition using only 62 features

frontal face authentication using morphological elastic graph matching a novel dynamic link architecture based on multiscale morphological dilation erosion is proposed for frontal face authentication instead of a set of gabor filters tuned to different orientations and scales multiscale morphological operations are employed to yield a feature vector at each node of the reference grid linear projection algorithms for feature selection and automatic weighting of the nodes according to their discriminatory power succeed to increase the authentication capability of the method the performance of the morphological dynamic link architecture is evaluated in terms of the receiver operating characteristic in the m2vts face image database the comparison with other frontal face authentication algorithms indicates that the morphological dynamic link architecture with discriminatory power coefficients is the best algorithm with respect to the equal error rate achieved