Face recognition using local features by LPP approach

An appearance-based face recognition approach called the Local Fisher Discriminant Embedding (LFDE) method is proposed in this paper. By using LFDE, the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information and obtains a face subspace that best detects the essential face manifold structure. Different from Locality Preserving Projections (LPP) and Unsupervised Discriminant projections (UDP) which ignore the class label information, LFDE searches for the project axes on which the data points of different classes are far from each other while requiring data points of the same class to be close to each other. We compare the proposed LFDE approach with PCA, FDA, LPP, and UDP on two different face databases. Experimental results suggest that the proposed LFDE approach provides a better representation and achieves higher accuracy in face recognition.

Face recognition technology has evolved as an enchanting solution to address the contemporary needs in order to perform identification and verification of identity claims. By advancing the feature extraction methods and dimensionality reduction techniques in the application of pattern recognition, a number of face recognition systems has been developed with distinct degrees of success. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP preserve the local structure of face image space which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). This paper focuses on a systematic analysis of locality-preserving projections and the application of LPP in combination with an existing technique This combined approach of LPP through MPCA can preserve the global and the local structure of the face image which is proved very effective. Proposed approach is tested using the AT & T face database. Experimental results show the significant improvements in the face recognition performance in comparison with some previous methods.

Locality Preserving Projections (LPP) is a linear projective map that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many fields, it has limits to solve recognition problem. Thus, a new palmprint recognition method is proposed based on Kernel Locality Preserving Projections (KLPP). Different from LPP method, KLPP not only describes the nonlinear correlations between pixels, but also preserves the local structure of the palmprint image space. In this way, the unwanted variations resulting from in lighting may be eliminated or reduced. We compare our proposed approach with Principal Component Analysis (PCA), LPP and Kernel Principal Component Analysis (KPCA) methods on PolyU palmprint database. Experiment results demonstrate that KLPP achieves better recognition rate as the dimension of the palmprint subspace changes.

Quaternion-based improved LPP method for color face recognition

This paper proposes a new pattern classification scheme, combining global and local features. The proposed method uses principal component analysis (PCA) for global property and locality preserving projections (LPP) for local property of the pattern. PCA is known for preserving the most descriptive ones after projection while LPP is known for preserving the neighborhood structure of the data set. Our combing method integrates global and local descriptive information and finds a richer set of alternatives beyond PCA and LPP in a 2-D parametric space. In order to find the hybrid features adaptively and find optimal parameters, we employ the genetic algorithm (GA). Experiments are performed with UCI machine learning repository to show the performance of the proposed algorithm.

With the deployment of collaborative robotics in industrial and domestic applications, intelligent robots are anticipated to work with humans in dynamically challenging daily life environments. Robots must deal with several photometric and spatio-temporal complexity in the workspace to achieve successful human-robot collaboration. To overcome these issues, especially in the case of vision-sensor-based systems, several robustness and adaptability measures need to be incorporated. One of such could be the adaptation of a concise and distinctive feature extraction tool for information-rich, high-dimensional data such as locality preserving projections (LPP). The locality information of the original higher-dimensional data is encoded and preserved in that weight parameter unlike principal component analysis (PCA) and linear discriminant analysis (LDA). This can be very much useful in real-life vision-sensor-based systems where the intrinsic locality structure of the images carries significant information. This paper proposes an LPP-based solution for a real-life human-supervised robot navigation guidance system. The proposed approach outperforms contemporary feature extraction techniques like PCA.

Learning representations that disentangle hidden explanatory factors in data has proven beneficial for effective pattern classification. Slow feature analysis (SFA) is a nonlinear dimensionality reduction technique that provides a useful representation for classification if the training data is sequential and transitions between classes are rare. The pattern discrimination ability of SFA has been attributed to the equivalence of linear SFA and linear discriminant analysis (LDA) under certain conditions. LDA, however, is often outperformed by locality preserving projections (LPP) when the data lies on or near a low-dimensional manifold. Here, we take a unified manifold learning perspective on LPP, LDA and SFA. We suggest that the discrimination ability of SFA is better explained by its relation to LPP than to LDA, and give an example of a situation where linear SFA outperforms LDA. We then propose a novel supervised manifold learning architecture that combines hierarchical nonlinear expansions, as commonly used for SFA, with supervised LPP. It learns a nonlinear parametric data representation that explicitly takes both the class labels and the manifold structure of the data into account. As an experimental validation, we show that this approach outperforms previously proposed models on the NORB object recognition dataset.

Facial feature selection is one of the most important challenges in a face recognition system. In a face image, only a part of a face image is changed due to illumination, pose, and other source of changes. In this paper, a novel face recognition approach is proposed based on fusing global and local features. To extract global and local features, we employed Gabor wavelet filter to apply on whole image and non-overlapping sub-images with equal size. To reduce the dimension of new fused feature vector, Principal Component Analysis (PCA) technique is employed. In our experiments, we used KNN and multi-class SVM classifiers and ORL database to obtain face recognition rate. The results show that the new face recognition algorithm outperforms the conventional methods such as global Gabor face recognition, and G-2DFLD feature fusion face recognition in term of recognition rate.

Lorentzian geometry is a subject of mathematics and has famous applications in physics, especially in relativity theory. This geometry has interesting features, e.g. one axis has a negative sign in metric definition (time axis). In this study, we try to apply Lorentzian geometry for feature extraction and dimensionality reduction. We use a Lorentzian Manifold (LM) for face recognition and reduce the dimensionality in this new feature space. We compare results with different feature extraction methods; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). Our experiments show that the best feature extraction method is LM and it produces the best face recognition rates. It is also powerful in dimensionality reduction.

Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.

In this paper a hybrid technique is used for determining the face from an image. Face detection is one of the tedious job to achieve with very high accuracy. In this paper we proposed a method that combines two techniques that is Orthogonal Laplacianface (OLPP) and Particle Swarm Optimization (PSO). The formula for the OLPP relies on the Locality Preserving Projection (LPP) formula, which aims at finding a linear approximation to the Eigen functions of the astronomer Beltrami operator on the face manifold. However, LPP is non-orthogonal and this makes it difficult to reconstruct the information. When the set of features is found by the OLPP, with the help of the PSO, the grouping of the image features is done and the one with the best match from the database is given as the result. This hybrid technique gives a higher accuracy in less processing time.

Local appearance-based methods have been proposed recently for face recognition. We analyze the effects of different dimension reduction and normalization methods on local appearance-based face recognition in this paper. Each image is divided into equal sized blocks and six different dimension reduction methods are implemented for each block separately to create local visual feature vectors. On these local features, several normalization methods are applied in an attempt to eliminate the changes in lighting conditions and contrast differences among blocks of different face images. The experimental results show the improvements in recognition rates due to the effects of dimension reduction and normalization for three different classifiers. Usage of trainable dimension reduction methods instead of DCT and a new normalization method in our work (within-block normalization as referred in this paper) are two factors that makes difference from previous works in literature. The best performance is achieved using a block size of 16times16, performing dimension reduction using approximate pairwise accuracy criterion (aPAC) and applying within-block mean and variance normalization.

A novel dimensionality reduction algorithm named “global–local preserving projections” (GLPP) is proposed. Different from locality preserving projections (LPP) and principal component analysis (PCA), GLPP aims at preserving both global and local structures of the data set by solving a dual-objective optimization function. A weighted coefficient is introduced to adjust the trade-off between global and local structures, and an efficient selection strategy of this parameter is proposed. Compared with PCA and LPP, GLPP is more general and flexible in practical applications. Both LPP and PCA can be interpreted under the GLPP framework. A GLPP-based online process monitoring approach is then developed. Two monitoring statistics, i.e., D and Q statistics, are constructed for fault detection and diagnosis. The case study on the Tennessee Eastman process illustrates the effectiveness and advantages of the GLPP-based monitoring method.

Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimension reduction algorithms, including principal component analysis (PCA), local preserving projections (LPP), neighborhood preserving embedding (NPE), and linear discriminant analysis (LDA), were used to reduce hyperspectral dimensions, and their classification performances on the algae concentration levels in water samples using hyperspectral imaging were compared. The LPP model showed satisfactory classification accuracy of 94.296 %, which was superior to the results based on reducing spectral dimensions with LDA (94.118 %), NPE (93.353 %), and PCA (90.588 %). The results demonstrated the potential of hyperspectral imaging coupled with dimension reduction methods in classifying water bodies with different algae concentration levels.

Dimensionality reduction technologies are very important for pattern representation and recognition. Among them, locality preserving projection (LPP) is a manifold dimensionality reduction scheme and has been successfully applied to face recognition. However, LPP is an unsupervised linear approach, its performance will degrade for classification tasks. Especially, when the dimension of input space is greater than the number of training data, singularity problem will occur and LPP cannot be implemented directly. To tackle the draw backs of LPP algorithm, this paper proposes a novel regularized LPP(RLPP) approach using supervised graph and regularization technique. The proposed RLPP method has been tested and evaluated with two public available databases, namely ORL and FERET databases. Experimental results show that the proposed RLPP approach surpasses Laplacianface and Direct-LPP (DLPP) methods.