Face recognition based on feature level fusion

Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC) $^{2}\text{A}$ and Block FLDA.

Quaternion-based improved LPP method for color face recognition

Locality preserving projections(LPP) algorithm can only preserve nearest local quantity,so within-class and between-class local scatter matrices are introduced for characterizing the manifold structure better,and a method called uncorrelated locality preserving projection analysis based on effective and stable maximum margin criterion(MMC) is proposed.When maximizing the trace difference of scatter matrix,weight the within-class and between-class local scatter matrix through a regularized parameter so as to find the better classification subspace and avoid small sample problem.More importantly,the discriminant feature set based on the MMC is generally statistical correlated,which makes the feature information be redundant,so an uncorrelated constraint was added in the paper and the uncorrelated discriminant feature set is extracted by the derived formulas,which are more favorable for the correct recognition.Ultimately experiments on Yale,PIE face database and MNIST handwritten digit database show that the method in this paper is effective and stable and has a higher correct recognition rate compared with the LPP,LDA(Linear discriminant analysis) and LPMIP(Locality-preserved maximum information projection).

Local graph embedding based on maximum margin criterion via fuzzy set

In the present work, appearance-based face recognition method called the Laplacianface approach is used. The face images are mapped into a face subspace for analysis by using Locality Preserving Projections(LPP). The technique is different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space. The main goal of LPP is to preserve neighbourhood structure of the data set optimally and hence obtain a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the Eigen functions of the Laplace Beltrami operator on the face manifold. Hence, by using this approach undesired variations because of facial expression, changes in lighting conditions, and pose may be eliminated or reduced. Performance analysis of face recognition is carried out on standard databases using both LPP technique and PCA technique. For comparison purpose, PCA with ANN classifier based on Back Propagation Feed Forward Neural Network is also developed and being used for training the input face images and then testing. LPP approach outperforms PCA with ANN, and provides better face representation and also achieves lower error rate.

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 projection (LPP) is a promising manifold-based dimensionality reduction and linear feature extraction method for face recognition. However, there exist two main issues in traditional LPP algorithm. LPP does not utilize the class label information at the training stage and its performance will be affected for classification tasks. In addition, LPP often suffers from small sample size (3S) problem, which occurs when the dimension of input pattern space is greater than the number of training samples. Under this situation, LPP fails to work. To overcome these two limitations, this paper presents a novel supervised regularization LPP (SRLPP) approach based on a supervised graph and a new regularization strategy. It theoretically proves that regularization matrix [Formula: see text] approaches to the original one as the regularized parameter tends to zero. The proposed SRLPP method is subsequently applied to face recognition. The experiments are conducted on two publicly available face databases, namely ORL database and FERET database. Compared with some existing LDA-based and LPP-based linear feature extraction approaches, experimental results show that our SRLPP approach gives superior performance.

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.

LPP solution schemes for use with face recognition

When locality preserving projection (LPP) method was originally proposed, it takes as the LPP solution the minimum eigenvalue solution of an eigenequation. After that, LPP has been used for image recognition problems such as face recognition. However, almost no researcher realizes that LPP usually encounters several difficulties when applied to the image recognition problem. For example, since image recognition problems are usually small sample size (SSS) problems, the corresponding eigenequation cannot be directly solved. In addition, it seems that even if one can obtain the solution of the eigenequation by using the numerical analysis approach, the obtained conventional LPP solution might produce the `presentation confusion' problem for samples from different classes, which is disadvantageous for the classification to procedure a high accuracy. In this paper we first thoroughly investigate the characteristics and drawbacks of the conventional LPP solution in the small sample size (SSS) problem in which the sample number is smaller than the data dimension. In order to overcome these drawbacks, we propose a new LPP solution for the SSS problem, which has clear physical meaning and can be directly and easily worked out because it is generated from a non-singular eigenequation. Experimental results the proposed solution scheme can produce a much lower classification error rate than the conventional LPP solution.

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.

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.

With regard to the high dimensional and small sample facial feature, this paper introduced the Kernel and Canonical Correlation Analysis (CCA) into the Locality Preserving Projections (LPP) algorithm and proposed a new face recognition algorithm based on the Kernel Base Locality Preserving Canonical Correlation Analysis (KLPCCA) with the derivation process. According to this algorithm, first use CCA to extract nonlinear information of a face image, and then make a linear mapping through LPP, at last conduct the calculation process taking into consideration of kernel and other data obtained before, so as to carry out the face recognition more accurately and simply.

Enhanced and parameterless Locality Preserving Projections for face recognition

<abstract> <sc><b>Abstract.</b> </sc>Crop disease seriously affects the yield and quality of crops and cause farmers to experience economic losses. Automatic detection of crop disease is an essential research topic to detect the disease symptoms as soon as they appear on the leaves. For crop disease management, it is important to automatically detect the crop diseases at a stage so as to treat them properly. In subspace dimension reduction-based data classification methods, the main topic is to design the between-class and within-class scatter matrices to obtain the mapping matrix from the training samples. But, there is much difference between the training samples. To eliminate the difference in designing the scatter matrices, in this article, based on the prior probability, a probability maximum margin criterion (MMC) is proposed for leaf spot image processing. Compared to the traditional MMC algorithm, in the proposed method, the two scatter matrices are expressed by the weighted mean vector of all training samples, which can overcome the influence of the outliers and the noise points, meanwhile improve the crop disease recognition rate. In this study, more than 100 classifying features are extracted from the disease leaf images of two kinds of cucumber diseases; probability MMC is performed for reduced dimensions in feature data processing, and then K-nearest-neighbor classifier is used to identify the cucumber diseases. The experimental results on a cucumber disease leaf image database show that the proposed method is effective for crop disease recognition.