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

the eikonal equation some results applicable to computer vision in this paper we investigate certain first order partial differential equations which formulate the relationship between the light reflected from a surface and its shape particular emphasis is given to eikonal equations two results are presented first we prove that a special type of eikonal equation has only one convex and positive c2 solution in some neighborhood of a singular point using this result we show that a restricted form of this equation has exactly two solutions these results have application in scanning electron microscopy

enhanced local texture feature sets for face recognition under difficult lighting conditions making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems we tackle this by combining the strengths of robust illumination normalization local texture based face representations distance transform based matching kernel based feature extraction and multiple feature fusion specifically we make three main contributions 1 we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition 2 we introduce local ternary patterns ltp a generalization of the local binary pattern lbp local texture descriptor that is more discriminant and less sensitive to noise in uniform regions and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of lbp ltp based face recognition and 3 we further improve robustness by adding kernel principal component analysis pca feature extraction and incorporating rich local appearance cues from two complementary sources gabor wavelets and lbp showing that the combination is considerably more accurate than either feature set alone the resulting method provides state of the art performance on three data sets that are widely used for testing recognition under difficult illumination conditions extended yale b cas peal r1 and face recognition grand challenge version 2 experiment 4 frgc 204 for example on the challenging frgc 204 data set it halves the error rate relative to previously published methods achieving a face verification rate of 88 1 at 0 1 false accept rate further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets data sets and lighting conditions

subdivision direction selection in interval methods for global optimization the role of the interval subdivision selection rule is investigated in branch and bound algorithms for global optimization the class of rules that allows convergence for the model algorithm is characterized and it is shown that the four rules investigated satisfy the conditions of convergence a numerical study with a wide spectrum of test problems indicates that there are substantial differences between the rules in terms of the required cpu time the number of function and derivative evaluations and space complexity and two rules can provide substantial improvements in efficiency

statistical approach to shape from shading reconstruction of three dimensional face surfaces from single two dimensional images the human visual system is proficient in perceiving three dimensional shape from the shading patterns in a two dimensional image how it does this is not well understood and continues to be a question of fundamental and practical interest in this paper we present a new quantitative approach to shape from shading that may provide some answers we suggest that the brain through evolution or prior experience has discovered that objects can be classified into lower dimensional object classes as to their shape extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low dimensional space we carry out this proposal for an important class of three dimensional 3d objects human heads from an ensemble of several hundred laser scanned 3d heads we use principal component analysis to derive a low dimensional parameterization of head shape space an algorithm for solving shape from shading using this representation is presented it works well even on real images where it is able to recover the 3d surface for a given person maintaining facial detail and identity from a single 2d image of his face this algorithm has applications in face recognition and animation

uniqueness in shape from shading for general images of smooth objects wholly contained in the field of view and for illumination symmetric around the viewing direction it is proven that shape is uniquely determined by shading thus shape from shading is a well posed problem under these illumination conditions and regularization is unnecessary for surface reconstruction and should be avoided generic properties of surfaces and images are established questions of existence are also discussed under the conditions above it is argued that most images are effectively impossible with no corresponding physically reasonable surface and that any image can be rendered effectively impossible by a small perturbation of its intensities this is explicitly illustrated for a synthetic image the proofs are based on ideas of dynamical systems theory and global analysis

scale affine invariant interest point detectors in this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations our scale and affine invariant detectors are based on the following recent results 1 interest points extracted with the harris detector can be adapted to affine transformations and give repeatable results geometrically stable 2 the characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives the laplacian 3 the affine shape of a point neighborhood is estimated based on the second moment matrix our scale invariant detector computes a multi scale representation for the harris interest point detector and then selects points at which a local measure the laplacian is maximal over scales this provides a set of distinctive points which are invariant to scale rotation and translation as well as robust to illumination changes and limited changes of viewpoint the characteristic scale determines a scale invariant region for each point we extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood an iterative algorithm modifies location scale and neighborhood of each point and converges to affine invariant points this method can deal with significant affine transformations including large scale changes the characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point we present a comparative evaluation of different detectors and show that our approach provides better results than existing methods the performance of our detector is also confirmed by excellent matching resultss the image is described by a set of scale affine invariant descriptors computed on the regions associated with our points

asymptotically stable interval observers for planar systems with complex poles in some parametric domains the problem of designing an exponentially stable interval observer for an exponentially stable two dimensional time invariant linear system is open we show that in some cases no linear time invariant change of coordinates can help to determine an exponentially stable interval observer next we solve the problem by constructing interval observers of a new type which have as key feature the property of being time varying this new design is applied to the chaotic chua s system

lambertian reflectance and linear subspaces we prove that the set of all lambertian reflectance functions the mapping from surface normals to intensities obtained with arbitrary distant light sources lies close to a 9d linear subspace this implies that in general the set of images of a convex lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low dimensional linear subspace explaining prior empirical results we also provide a simple analytic characterization of this linear space we obtain these results by representing lighting using spherical harmonics and describing the effects of lambertian materials as the analog of a convolution these results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions we also show a simple way to enforce nonnegative lighting when the images of an object lie near a 4d linear space we apply these algorithms to perform face recognition by finding the 3d model that best matches a 2d query image

a robust real time algorithm for facial shape recovery from a single image containing cast shadow under general unknown lighting it is difficult to find a non iterative solution for statistical shape from shading sfs for a single image containing cast shadow under unknown arbitrary light conditions this is because it is not trivial to find a mapping from a convex cone in the image space to a point in the depth space to find a non iterative solution for the statistical sfs which has not been done yet to our knowledge we show that it is possible to approximate a cone of images illumination cone to a polytope by a nonlinear function which is a transform from cartesian coordinates to hyperspherical coordinates the images of a subject form a convex cone in the image space and it can be better to use the direction of an image in hyperspherical coordinates as an input feature for the mapping rather than the image itself the maximum error occurs on one of the vertices of the polytope if we choose the objective function to be the squared error of a linear function in the hyperspherical space hence we can solve the least square problem using the cost only for the vertices in deriving the solution we use the generalized rayleigh quotient canonical correlation analysis cca and prior information on input images to reduce the solution space and improve 3 d reconstruction performance in experiments the proposed scheme performs robustly under light variations and is fast enough for real time applications

symmetric shape from shading using self ratio image in this paper we present a symmetric shape from shading sfs approach to recover both shape and albedo for symmetric objects lambertian surfaces with unknown varying albedo and orthographic projections are assumed in our formulation of symmetric sfs we have two image irradiance equations one is the standard equation used in sfs and the other is a self ratio image irradiance equation this new image irradiance equation relates the self ratio image which is defined as the ratio of two halves of the input image to light source and surface shape the introduction of the self ratio image facilitates the direct use of symmetry cue based on the self ratio image a new model based symmetric source from shading algorithm is also presented we then propose symmetric sfs algorithms to recover both shape and albedo from a single image and present experimental results the new symmetric sfs scheme has one important property the existence of a unique global solution which consists of unique local solutions at each point simultaneously obtained using the intensity information at that point and the surrounding local region and the assumption of a cal c 2 surface proofs for the existence of a unique solution in the cases of unknown constant and non constant albedos are provided

a method for enforcing integrability in shape from shading algorithms an approach for enforcing integrability a particular implementation of the approach an example of its application to extending an existing shape from shading algorithm and experimental results showing the improvement that results from enforcing integrability are presented a possibly nonintegrable estimate of surface slopes is represented by a finite set of basis functions and integrability is enforced by calculating the orthogonal projection onto a vector subspace spanning the set of integrable slopes the integrability projection constraint was applied to extending an iterative shape from shading algorithm of m j brooks and b k p horn 1985 experimental results show that the extended algorithm converges faster and with less error than the original version good surface reconstructions were obtained with and without known boundary conditions and for fairly complicated surfaces

recognizing imprecisely localized partially occluded and expression variant faces from a single sample per class the classical way of attempting to solve the face or object recognition problem is by using large and representative data sets in many applications though only one sample per class is available to the system in this contribution we describe a probabilistic approach that is able to compensate for imprecisely localized partially occluded and expression variant faces even when only one single training sample per class is available to the system to solve the localization problem we find the subspace within the feature space e g eigenspace that represents this error for each of the training images to resolve the occlusion problem each face is divided into k local regions which are analyzed in isolation in contrast with other approaches where a simple voting space is used we present a probabilistic method that analyzes how good a local match is to make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image

illumination invariant single face image recognition under heterogeneous lighting condition illumination problem is still a bottleneck of robust face recognition system which demands extracting illumination invariant features in this field existing works only consider the variations caused by lighting direction or magnitude denoted as homogeneous lighting but the effect of spectral wavelength is always ignored and thus existing illumination invariant descriptors have its limitation on processing face images under different spectral wavelengths denoted as heterogeneous lighting we propose a novel gradient based descriptor namely logarithm gradient histogram lgh which takes the illumination direction magnitude and the spectral wavelength together into consideration so that it can handle both homogeneous and heterogeneous lightings our proposal contributes in three folds 1 we incorporate lmsn log filter to eliminate the lighting effect of each image and extract two illumination invariant components namely logarithm gradient orientation lgo and logarithm gradient magnitude lgm 2 we propose an effective post processing strategy to make our model tolerant to noise and generate a histogram representation to integrate both lgo and lgm 3 we present solid theoretical analysis on the illumination invariant properties of our proposed descriptors extensive experimental results on cmu pie extended yaleb frgc and hfb databases are reported to verify the effectiveness of our proposed model highlightstwo illumination invariant components namely logarithm gradient orientation lgo and logarithm gradient magnitude lgm are extracted an effective post processing strategy is proposed to integrate both lgo and lgm generating the logarithm gradient histogram lgh solid theoretical analysis on the illumination invariant properties of the proposed descriptors is presented competitive results are reported both in homogeneous and heterogeneous lighting conditions

calculating the hausdorff dimension of tree structures the dimension of certain tree structures is of importance in percolation theory as well as in the theoretical treatment of many other branching processes we present a method of determining the hausdorff dimension of such structures by employing the technique of mauldin and williams the dimension is calculated based on the probability of generation of each branch from its parent on the tree representing the process we use this method to analyze the dimension of tree structures representing two directional linear bonding between equally weighted monomers and show how it can be used to model enzymatic reaction pathways

singular value decomposition and local near neighbors for face recognition under varying illumination illumination processing is a challenging task in face recognition although numerous techniques have been proposed to tackle this problem none of them can achieve high performance under different illumination variations in this paper we propose three closely related illumination processing techniques to address different illumination variations first we employ singular value decomposition svd bases with frequency interpretation and corresponding contribution coefficients i e normalized nonlinear singular values to obtain the high frequency facial features of a face image in logarithm domain which is termed as the high frequency svd face hfsvd face moreover we propose the adaptive hfsvd face ahfsvd face which adaptively selects a special nonlinear parameter to generate hfsvd face according to the face image illumination level the illumination level of a face image is estimated by its singular values second we construct the local near neighbor feature lnn feature based on the local region which is an illumination invariant measure then the discriminative weights dws and gaussian weights gws respectively combine with several lnn features to generate the fusion lnn feature flnn feature which can be named as dwflnn feature and gwlnn feature the bipolar sigmoidal function removes the high frequency interference of flnn feature to form lnn face including dwlnn face and gwlnn face third we develop the h l svd as a preprocessing technique which consists of the normalized low frequency components and the corrected high frequency components divided by svd and then the illumination insensitive measure can be extracted from h l svd by lnn face to robustly tackle the face image with severe illumination variations the performances of the proposed methods are verified using yale b cmu pie lfw and our self built driver face databases the experimental results indicate that the proposed methods can obtain high performances under different illumination variations and outperform several state of the art approaches which are proposed to address face recognition under varying illumination hfsvd face consists of svd bases and normalized nonlinear singular values ahfsvd face is the hfsvd face with a special nonlinear parameter lnn face is extracted from several local regions

height and gradient from shading the method described here for recovering the shape of a surface from a shaded image can deal with complex wrinkled surfaces integrability can be enforced easily because both surface height and gradient are represented the robustness of the method stems in part from linearization of the reflectance map about the current estimate of the surface orientation at each picture cell the new scheme can find an exact solution of a given shape from shading problem even though a regularizing term is included this is a reflection of the fact that shape from shading problems are it not ill posed when boundary conditions are available or when the image contains singular points

learning discriminant face descriptor local feature descriptor is an important module for face recognition and those like gabor and local binary patterns lbp have proven effective face descriptors traditionally the form of such local descriptors is predefined in a handcrafted way in this paper we propose a method to learn a discriminant face descriptor dfd in a data driven way the idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people in particular we propose to enhance the discriminative ability of face representation in three aspects first the discriminant image filters are learned second the optimal neighborhood sampling strategy is soft determined third the dominant patterns are statistically constructed discriminative learning is incorporated to extract effective and robust features we further apply the proposed method to the heterogeneous cross modality face recognition problem and learn dfd in a coupled way coupled dfd or c dfd to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem extensive experiments on feret cas peal r1 lfw and hfb face databases validate the effectiveness of the proposed dfd learning on both homogeneous and heterogeneous face recognition problems the dfd improves poem and lqp by about 4 5 percent on lfw database and the c dfd enhances the heterogeneous face recognition performance of lbp by over 25 percent

face recognition from a single training image under arbitrary unknown lighting using spherical harmonics in this paper we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation which require only one training image per subject and no 3d shape information our methods are based on the result which demonstrated that the set of images of a convex lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low dimensional linear subspace we provide two methods to estimate the spherical harmonic basis images spanning this space from just one image our first method builds the statistical model based on a collection of 2d basis images we demonstrate that by using the learned statistics we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation compared to the first method the second method builds the statistical models directly in 3d spaces by combining the spherical harmonic illumination representation and a 3d morphable model of human faces to recover basis images from images across both poses and illuminations after estimating the basis images we use the same recognition scheme for both methods we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image we provide a series of experiments that achieve high recognition rates under a wide range of illumination conditions including multiple sources of illumination our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements comparison of the two methods is also provided

discriminative multimanifold analysis for face recognition from a single training sample per person conventional appearance based face recognition methods usually assume that there are multiple samples per person mspp available for discriminative feature extraction during the training phase in many practical face recognition applications such as law enhancement e passport and id card identification this assumption however may not hold as there is only a single sample per person sspp enrolled or recorded in these systems many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning to address this problem we propose in this paper a novel discriminative multimanifold analysis dmma method by learning discriminative features from image patches first we partition each enrolled face image into several nonoverlapping patches to form an image set for each sample per person then we formulate the sspp face recognition as a manifold manifold matching problem and learn multiple dmma feature spaces to maximize the manifold margins of different persons finally we present a reconstruction based manifold manifold distance to identify the unlabeled subjects experimental results on three widely used face databases are presented to demonstrate the efficacy of the proposed approach

interval methods and contractor based branch and bound procedures for verified parameter identification of quasi linear cooperative system models abstract a large number of dynamic system models in engineering as well as computational physics chemistry or biology are described after first principle modeling approaches by sets of either discrete time difference equations or by sets of ordinary differential equations in both cases the structure of those system models results from fundamental physical system properties such as conservation laws of mass momentum and energy newton s or kirchhoff s laws as well as a structural subdivision into subsystems or individual compartments regardless of the area of application all of these system models involve parameters which are not perfectly known due to uncertainty resulting from tolerances in the construction of the respective real life systems or which are described by an inherent uncertainty coming from model simplifications or even from a lack of knowledge about detailed microscopic dynamic effects this is especially true for applications in engineering where a trade off between the model accuracy and the resulting computational complexity has to be made in order to obtain mathematical representations which can be evaluated numerically in real time this real time applicability is inevitable if tasks such as feedback control or model based state estimation on the basis of incomplete state measurements are of interest hence all aforementioned tasks depend on the reliable estimation of those parameters of the structurally predefined sets of state equations which are feasible with respect to the knowledge about selected measurable state variables under consideration of the associated measurement tolerances this paper presents computational techniques for a model based identification of system parameters under the a priori knowledge of stability of the underlying open loop dynamics and the structural constraint of cooperativity

a new method for the nonlinear transformation of means and covariances in filters and estimators this paper describes a new approach for generalizing the kalman filter to nonlinear systems a set of samples are used to parametrize the mean and covariance of a not necessarily gaussian probability distribution the method yields a filter that is more accurate than an extended kalman filter ekf and easier to implement than an ekf or a gauss second order filter its effectiveness is demonstrated using an example

face recognition by exploring information jointly in space scale and orientation information jointly contained in image space scale and orientation domains can provide rich important clues not seen in either individual of these domains the position spatial frequency and orientation selectivity properties are believed to have an important role in visual perception this paper proposes a novel face representation and recognition approach by exploring information jointly in image space scale and orientation domains specifically the face image is first decomposed into different scale and orientation responses by convolving multiscale and multi orientation gabor filters second local binary pattern analysis is used to describe the neighboring relationship not only in image space but also in different scale and orientation responses this way information from different domains is explored to give a good face representation for recognition discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques extensive experimental results on feret ar and frgc ver 2 0 databases show the significant advantages of the proposed method over the existing ones

design of interval observers and controls for pdes using finite element approximations synthesis of interval state estimators is investigated for the systems described by a class of parabolic partial differential equations pdes first a finite element approximation of a pde is constructed and the design of an interval observer for the derived ordinary differential equation is given second the interval inclusion of the state function of the pde is calculated using the error estimates of the finite element approximation finally the obtained interval estimates are used to design a dynamic output stabilizing control the results are illustrated by numerical experiments with an academic example and the black scholes model of financial market

numerical shape from shading and occluding boundaries an iterative method for computing shape from shading using occluding boundary information is proposed some applications of this method are shown we employ the stereographic plane to express the orientations of surface patches rather than the more commonly used gradient space use of the stereographic plane makes it possible to incorporate occluding boundary information but forces us to employ a smoothness constraint different from the one previously proposed the new constraint follows directly from a particular definition of surface smoothness we solve the set of equations arising from the smoothness constraints and the image irradiance equation iteratively using occluding boundary information to supply boundary conditions good initial values are found at certain points to help reduce the number of iterations required to reach a reasonable solution numerical experiments show that the method is effective and robust finally we analyze scanning electron microscope sem pictures using this method other applications are also proposed

probability distribution for percolation clusters generated on a cayley tree at criticality we present analytical and numerical results for the probability distributions of the number of sitess as a function of the number of shellsl for several ensembles of percolation clusters generated on a cayley tree at criticality we find that for the incipient infinite percolation cluster the probability distribution isp s l s l4 exp as l2 for s l 1

interval state estimation for a class of nonlinear systems the goal of this technical note is to design interval observers for a class of nonlinear continuous time systems the first part of this work shows that it is usually possible to design an interval observer for linear systems by means of linear time invariant changes of coordinates even if the system is not cooperative this result is extended to a class of nonlinear systems using partial exact linearisations the proposed observers guarantee to enclose the set of system states that is consistent with the model the disturbances and the measurement noise moreover it is only assumed that the measurement noise and the disturbances are bounded without any additional information such as stationarity uncorrelation or type of distribution the proposed observer is illustrated through numerical simulations

parametric shape from shading by radial basis functions we present a new method of shape from shading by using radial basis functions to parameterize the object depth the radial basis functions are deformed by adjusting their centers widths and weights such that the intensity errors are minimized the initial centers and widths are arranged hierarchically to speed up convergence and to stabilize the solution although the smoothness constraint is used it can be eventually dropped out without causing instabilities in the solution an important feature of our parametric shape from shading method is that it offers a unified framework for integration of multiple sensory information we show that knowledge about surface depth and or surface normals anywhere in the image can be easily incorporated into the shape from shading process it is further demonstrated that even qualitative knowledge can be used in shape from shading to improve 3d reconstruction experimental comparisons of our method with several existing ones are made by using both synthetic and real images results show that our solution is more accurate than the others

interval state observer for nonlinear time varying systems this paper is devoted to the design of interval observers for linear time varying ltv systems and a class of nonlinear time varying systems in the output canonical form an interval observer design is feasible if it is possible to calculate the observer gains making the estimation error dynamics cooperative and stable it is shown that under some mild conditions the cooperativity of an ltv system can be ensured by a static linear transformation of coordinates the efficiency of the proposed approach is demonstrated through numerical simulations

recovering facial shape using a statistical model of surface normal direction in this paper we show how a statistical model of facial shape can be embedded within a shape from shading algorithm we describe how facial shape can be captured using a statistical model of variations in surface normal direction to construct this model we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to cartesian points on a local tangent plane the distribution of surface normal directions is captured using the covariance matrix for the projected point positions the eigenvectors of the covariance matrix define the modes of shape variation in the fields of transformed surface normals we show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by lambert s law we demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details we assess the accuracy of the technique on a variety of images with ground truth and real world images

estimation of illuminant direction albedo and shape from shading a robust approach to the recovery of shape from shading information is presented assuming uniform albedo and lambertian surface for the imaging model two methods for estimating the azimuth of the illuminant are presented one is based on local estimates on smooth patches and the other method uses shading information along image contours the elevation of the illuminant and surface albedo are estimated from image statistics taking into consideration the effect of self shadowing with the estimated reflectance map parameters the authors then compute the surface shape using a procedure that implements the smoothness constraint by requiring the gradients of reconstructed density to be close to the gradients of the input image the algorithm is data driven stable updates the surface slope and height maps simultaneously and significantly reduces the residual errors in irradiance and integrability terms a hierarchical implementation of the algorithm is presented typical results on synthetic and images are given to illustrate the usefulness of the approach

histogram of gabor phase patterns hgpp a novel object representation approach for face recognition a novel object descriptor histogram of gabor phase pattern hgpp is proposed for robust face recognition in hgpp the quadrant bit codes are first extracted from faces based on the gabor transformation global gabor phase pattern ggpp and local gabor phase pattern lgpp are then proposed to encode the phase variations ggpp captures the variations derived from the orientation changing of gabor wavelet at a given scale frequency while lgpp encodes the local neighborhood variations by using a novel local xor pattern lxp operator they are both divided into the nonoverlapping rectangular regions from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image finally the recognition is performed by using the nearest neighbor classifier with histogram intersection as the similarity measurement the features of hgpp lie in two aspects 1 hgpp can describe the general face images robustly without the training procedure 2 hgpp encodes the gabor phase information while most previous face recognition methods exploit the gabor magnitude information in addition fisher separation criterion is further used to improve the performance of hgpp by weighing the subregions of the image according to their discriminative powers the proposed methods are successfully applied to face recognition and the experiment results on the large scale feret and cas peal databases show that the proposed algorithms significantly outperform other well known systems in terms of recognition rate