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

face recognition using the discrete cosine transform an accurate and robust face recognition system was developed and tested this system exploits the feature extraction capabilities of the discrete cosine transform dct and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination the method was tested on a variety of available face databases including one collected at mcgill university the system was shown to perform very well when compared to other approaches

comments on globally maximizing locally minimizing unsupervised discriminant projection with application to face and palm biometrics this paper develops an unsupervised discriminant projection udp technique for dimensionality reduction of high dimensional data in small sample size cases udp can be seen as a linear approximation of a multimanifolds based learning framework which takes into account both the local and nonlocal quantities udp characterizes the local scatter as well as the nonlocal scatter seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter this characteristic makes udp more intuitive and more powerful than the most up to date method locality preserving projection lpp which considers only the local scatter for clustering or classification tasks the proposed method is applied to face and palm biometrics and is examined using the yale feret and ar face image databases and the polyu palmprint database the experimental results show that udp consistently outperforms lpp and pca and outperforms lda when the training sample size per class is small this demonstrates that udp is a good choice for real world biometrics applications

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

face recognition using scale adaptive directional and textural features a novel approach to face recognition problem using directional and texture information from face images is proposed in this paper in order to capture the directionality specially designed using local polynomial approximation technique scale adaptive digital filters are used for texture features extraction a low dimensional and computationally effective local descriptor is utilized textural and directional features are captured at the holistic and part based levels resulting in a robust face descriptor the proposed method is tested on a number of standard test face datasets orl xm2vts extended yale cmu pie ar and feret for different scenarios and its performance is compared with several state of the art techniques

hierarchical ensemble of global and local classifiers for face recognition in the literature of psychophysics and neurophysiology many studies have shown that both global and local features are crucial for face representation and recognition this paper proposes a novel face recognition method which exploits both global and local discriminative features in this method global features are extracted from the whole face images by keeping the low frequency coefficients of fourier transform which we believe encodes the holistic facial information such as facial contour for local feature extraction gabor wavelets are exploited considering their biological relevance after that fisher s linear discriminant fld is separately applied to the global fourier features and each local patch of gabor features thus multiple fld classifiers are obtained each embodying different facial evidences for face recognition finally all these classifiers are combined to form a hierarchical ensemble classifier we evaluate the proposed method using two large scale face databases feret and frgc version 2 0 experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol

graph embedding and extensions a general framework for dimensionality reduction a large family of algorithms supervised or unsupervised stemming from statistics or geometry theory has been designed to provide different solutions to the problem of dimensionality reduction despite the different motivations of these algorithms we present in this paper a general formulation known as graph embedding to unify them within a common framework in graph embedding each algorithm can be considered as the direct graph embedding or its linear kernel tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided furthermore the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms by utilizing this framework as a tool we propose a new supervised dimensionality reduction algorithm called marginal fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class while the penalty graph connects the marginal points and characterizes the interclass separability we show that mfa effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions real face recognition experiments show the superiority of our proposed mfa in comparison to lda also for corresponding kernel and tensor extensions

gabor based kernel pca with doubly nonlinear mapping for face recognition with a single face image in this paper a novel gabor based kernel principal component analysis pca with doubly nonlinear mapping is proposed for human face recognition in our approach the gabor wavelets are used to extract facial features then a doubly nonlinear mapping kernel pca dkpca is proposed to perform feature transformation and face recognition the conventional kernel pca nonlinearly maps an input image into a high dimensional feature space in order to make the mapped features linearly separable however this method does not consider the structural characteristics of the face images and it is difficult to determine which nonlinear mapping is more effective for face recognition in this paper a new method of nonlinear mapping which is performed in the original feature space is defined the proposed nonlinear mapping not only considers the statistical property of the input features but also adopts an eigenmask to emphasize those important facial feature points therefore after this mapping the transformed features have a higher discriminating power and the relative importance of the features adapts to the spatial importance of the face images this new nonlinear mapping is combined with the conventional kernel pca to be called doubly nonlinear mapping kernel pca the proposed algorithm is evaluated based on the yale database the ar database the orl database and the yaleb database by using different face recognition methods such as pca gabor wavelets plus pca and gabor wavelets plus kernel pca with fractional power polynomial models experiments show that consistent and promising results are obtained

fusing local patterns of gabor magnitude and phase for face recognition gabor features have been known to be effective for face recognition however only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature to investigate the potential of gabor phase and its fusion with magnitude for face recognition in this paper we first propose local gabor xor patterns lgxp which encodes the gabor phase by using the local xor pattern lxp operator then we introduce block based fisher s linear discriminant bfld to reduce the dimensionality of the proposed descriptor and at the same time enhance its discriminative power finally by using bfld we fuse local patterns of gabor magnitude and phase for face recognition we evaluate our approach on feret and frgc 2 0 databases in particular we perform comparative experimental studies of different local gabor patterns we also make a detailed comparison of their combinations with bfld as well as the fusion of different descriptors by using bfld extensive experimental results verify the effectiveness of our lgxp descriptor and also show that our fusion approach outperforms most of the state of the art approaches

bayesian helmholtz stereopsis with integrability prior helmholtz stereopsis is a 3d reconstruction method uniquely independent of surface reflectance yet its sub optimal maximum likelihood formulation with drift prone normal integration limits performance via three contributions this paper presents a complete novel pipeline for helmholtz stereopsis first we propose a bayesian formulation replacing the maximum likelihood problem by a maximum a posteriori one second a tailored prior enforcing consistency between depth and normal estimates via a novel metric related to optimal surface integrability is proposed third explicit surface integration is eliminated by taking advantage of the accuracy of prior and high resolution of the coarse to fine approach the pipeline is validated quantitatively and qualitatively against alternative formulations reaching sub millimetre accuracy and coping with complex geometry and reflectance

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

geometric mean for subspace selection subspace selection approaches are powerful tools in pattern classification and data visualization one of the most important subspace approaches is the linear dimensionality reduction step in the fisher s linear discriminant analysis flda which has been successfully employed in many fields such as biometrics bioinformatics and multimedia information management however the linear dimensionality reduction step in flda has a critical drawback for a classification task with c classes if the dimension of the projected subspace is strictly lower than c 1 the projection to a subspace tends to merge those classes which are close together in the original feature space if separate classes are sampled from gaussian distributions all with identical covariance matrices then the linear dimensionality reduction step in flda maximizes the mean value of the kullback leibler kl divergences between different classes based on this viewpoint the geometric mean for subspace selection is studied in this paper three criteria are analyzed 1 maximization of the geometric mean of the kl divergences 2 maximization of the geometric mean of the normalized kl divergences and 3 the combination of 1 and 2 preliminary experimental results based on synthetic data uci machine learning repository and handwriting digits show that the third criterion is a potential discriminative subspace selection method which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in flda and its several representative extensions

square lattice ising model susceptibility connection matrices and singular behaviour of χ 3 and χ 4 we present a simple but efficient way to calculate connection matrices between sets of independent local solutions defined at two neighbouring singular points of fuchsian differential equations of quite large orders such as those found for the third and fourth contribution 3 and 4 to the magnetic susceptibility of the square lattice ising model we deduce all the critical behaviours of the solutions 3 and 4 as well as the asymptotic behaviour of the coefficients in the corresponding series expansions we confirm that the newly found quadratic singularities of the fuchsian ode associated with 3 are not singularities of the particular solution 3 itself we use the previous connection matrices to get the exact expressions of all the monodromy matrices of the fuchsian differential equation for 3 and 4 expressed in the same basis of solutions these monodromy matrices are the generators of the differential galois group of the fuchsian differential equations for 3 and 4 whose analysis is just sketched here as far as the physics implications of the solutions are concerned we find challenging qualitative differences when comparing the corrections to scaling for the full susceptibility at high temperature respectively low temperature and the first two terms 1 and 3 respectively 2 and 4

face recognition using laplacianfaces we propose an appearance based face recognition method called the laplacianface approach by using locality preserving projections lpp the face images are mapped into a face subspace for analysis different from principal component analysis pca and linear discriminant analysis lda which effectively see only the euclidean structure of face space lpp finds an embedding that preserves local information and obtains a face subspace that best detects the essential face manifold structure the laplacianfaces are the optimal linear approximations to the eigenfunctions of the laplace beltrami operator on the face manifold in this way the unwanted variations resulting from changes in lighting facial expression and pose may be eliminated or reduced theoretical analysis shows that pca lda and lpp can be obtained from different graph models we compare the proposed laplacianface approach with eigenface and fisherface methods on three different face data sets experimental results suggest that the proposed laplacianface approach provides a better representation and achieves lower error rates in face recognition

face recognition using holistic fourier invariant features abstract this paper presents a new method for holistic face representation called spectroface spectroface representation combines the wavelet transform and the fourier transform we have shown that by decomposing a face image using wavelet transform the low frequency face image is less sensitive to the facial expression variations this paper also proves that the spectroface representation is invariant to translation scale and on the plane rotation to handle the rotation in depth multiple view images are used to determine the reference image representation based on the spectroface representation a face recognition system is designed and developed yale and olivetti face databases are selected to evaluate the proposed system these two databases contain 55 persons with 565 face images at different orientations scale facial expressions small occlusions and different illuminations the recognition accuracy is over 94 if we consider the top three matches the accuracy is over 98 the recognition system is developed on pentium 200 mhz computer and the recognition time is less than 3 seconds for database with 55 persons

orthogonal laplacianfaces for face recognition following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space we propose an appearance based face recognition method called orthogonal laplacianface our algorithm is based on the locality preserving projection lpp algorithm which aims at finding a linear approximation to the eigenfunctions of the laplace beltrami operator on the face manifold however lpp is nonorthogonal and this makes it difficult to reconstruct the data the orthogonal locality preserving projection olpp method produces orthogonal basis functions and can have more locality preserving power than lpp since the locality preserving power is potentially related to the discriminating power the olpp is expected to have more discriminating power than lpp experimental results on three face databases demonstrate the effectiveness of our proposed algorithm

discriminant waveletfaces and nearest feature classifiers for face recognition feature extraction discriminant analysis and classification rules are three crucial issues for face recognition we present hybrid approaches to handle three issues together for feature extraction we apply the multiresolution wavelet transform to extract the waveletface we also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power during classification the nearest feature plane nfp and nearest feature space nfs classifiers are explored for robust decisions in presence of wide facial variations their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated in the experiments the discriminant waveletface incorporated with the nfs classifier achieves the best face recognition performance

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

capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance this paper presents a novel pattern recognition framework by capitalizing on dimensionality increasing techniques in particular the framework integrates gabor image representation a novel multiclass kernel fisher analysis kfa method and fractional power polynomial models for improving pattern recognition performance gabor image representation which increases dimensionality by incorporating gabor filters with different scales and orientations is characterized by spatial frequency spatial locality and orientational selectivity for coping with image variabilities such as illumination variations the kfa method first performs nonlinear mapping from the input space to a high dimensional feature space and then implements the multiclass fisher discriminant analysis in the feature space the significance of the nonlinear mapping is that it increases the discriminating power of the kfa method which is linear in the feature space but nonlinear in the input space the novelty of the kfa method comes from the fact that 1 it extends the two class kernel fisher methods by addressing multiclass pattern classification problems and 2 it improves upon the traditional generalized discriminant analysis gda method by deriving a unique solution compared to the gda solution which is not unique the fractional power polynomial models further improve performance of the proposed pattern recognition framework experiments on face recognition using both the feret database and the frgc face recognition grand challenge databases show the feasibility of the proposed framework in particular experimental results using the feret database show that the kfa method performs better than the gda method and the fractional power polynomial models help both the kfa method and the gda method improve their face recognition performance experimental results using the frgc databases show that the proposed pattern recognition framework improves face recognition performance upon the bee baseline algorithm and the lda based baseline algorithm by large margins

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