Two-dimensional Principal Component Analysis Research Articles

Features Fusion Based on FLD for Face Recognition

In this paper, we introduced a features fusion method for face recognition based on Fisher’s Linear Discriminant (FLD). The method extract features by employed Two-Dimensional principal component analysis (2DPCA) and Gabor wavelets, and then fuse their features which are extracted with FLD respectively. As a holistic feature extraction method, 2DPCA performs dimensional reduction to the input dataset while retaining characteristics of the dataset that contribute most to its variance by eliminating the later principal components. On the contrary, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, which produce salient local features which are most suitable for face recognition. So, we use Gabor wavelets for the local features and then integrate Gabor features with 2DPCA features. In addition to, because FLD could make not only the scatter between classes as large as possible, but the scatter within class as small as possible, the features which are extracted by FLD are reliable for classification. And then, the FLD features of the Gabor and 2DPCA features leads to the application of the support vector machine (SVM) for classification. Finally, the computer simulation illustrates the effectivity of this method on three independent face databases.

L1-Norm-Based 2DPCA

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.

Computationally efficient algorithm for face super-resolution using (2D)2-PCA based prior

Super-resolution algorithms typically transform images into 1D vectors and operate on these vectors to obtain a high-resolution image. In this study, the authors first propose a 2D method for super-resolution using a 2D model that treats images as matrices. We then apply this 2D model to the super-resolution of face images. Two-directional two-dimensional principal component analysis (PCA) [(2D) 2 -PCA] is an efficient face representation technique where the images are treated as matrices instead of vectors. We use (2D) 2 -PCA to learn the face subspace and use it as a prior to super-resolve face images. Experimental results show that our approach can reconstruct high quality face images with low computational cost.

Structural two-dimensional principal component analysis for image recognition

In this paper, a new technique called structural two-dimensional principal component analysis (S2DPCA) is proposed for image recognition. S2DPCA is a subspace learning method that identifies the structural information for discrimination. Different from conventional two-dimensional principal component analysis (2DPCA) that only reflects within-row information of images, the goal of S2DPCA is to discover structural discriminative information contained in both within-row and between-row of the images. By contrast with 2DPCA, S2DPCA is directly based on the augmented images encoding corresponding row membership, and the projection directions of S2DPCA are obtained by solving an eigenvalue problem of the augmented image covariance matrix. Computationally, S2DPCA is straightforward and comparative with 2DPCA. Like 2DPCA, the singularity problem is completely avoided in S2DPCA. Experiments on face recognition and handwritten digit recognition are presented to show the effectiveness of the proposed approach.

Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning

This paper proposes an uncorrelated multilinear principal component analysis (UMPCA) algorithm for unsupervised subspace learning of tensorial data. It should be viewed as a multilinear extension of the classical principal component analysis (PCA) framework. Through successive variance maximization, UMPCA seeks a tensor-to-vector projection (TVP) that captures most of the variation in the original tensorial input while producing uncorrelated features. The solution consists of sequential iterative steps based on the alternating projection method. In addition to deriving the UMPCA framework, this work offers a way to systematically determine the maximum number of uncorrelated multilinear features that can be extracted by the method. UMPCA is compared against the baseline PCA solution and its five state-of-the-art multilinear extensions, namely two-dimensional PCA (2DPCA), concurrent subspaces analysis (CSA), tensor rank-one decomposition (TROD), generalized PCA (GPCA), and multilinear PCA (MPCA), on the tasks of unsupervised face and gait recognition. Experimental results included in this paper suggest that UMPCA is particularly effective in determining the low-dimensional projection space needed in such recognition tasks.

Increasing IgG Concentration Modulates the Conformational Heterogeneity and Bonding Network that Influence Solution Properties

Multiple molecular driving forces mediate protein stability, association, and recognition in concentrated solutions. Here we investigate the interactions that modulate the nonideal solution behavior of two immunoglobulins (IgG1s) in highly concentrated solutions using two-dimensional vibrational correlation spectroscopy (2D-COS) and principal components analysis (PCA). A specific sequence of changes is observed in the concentration-dependent vibrational spectra of the highly viscous IgG solution that deviates from ideality, whereas that sequence is reversed for all other conditions examined. The asynchronous spectra reveal variation in beta-sheet and turn regions occur before intensity variations in disordered and alpha-helical regions as the concentration is increased for the highly viscous regime. This is in contrast to the sequence observed for all other conditions studied and to the idea that beta-sheet regions are resistant to concentration-dependent affects. Finally, we show that increased hydrogen bonding and electrostatics primarily modulate the intermolecular association and nonideal behavior. Specifically, 2D-COS and PCA analysis of the amide II region suggests that Glu and Asp residues trigger the change resulting in increased viscosity and association of one IgG.

Wake Detection Using 2DPCA of Directional Polar Fourier Spectrum

A wake detection method is proposed for the water wake of airphotoes based on two-dimensional principal component analysis(2DPCA)of directional polar Fourier spectrum.This method improves the traditional principal components analysis to obtain the image direction from their Fourier power spectrum,and transforms the Fourier spectrum into the polar coordinates based on the image direction.The directional polar Fourier spectrum are translation and rotation invariant.Compared to the previous method of partitioning the polar spectrum to achieve texture features,the row 2DPCA,the column 2DPCA,and the improved 2DPCA are used to analysis the directional polar Fourier spectrum. From experimental results of 40 images,it is proved that the proposed algorithm can fetch the wake texture precisely.

Fusion of multiple facial regions for expression-invariant gender classification

A novel gender classification method is presented which fuses information acquired from multiple facial regions for improving overall performance. It is able to compensate for facial expression even when training samples contain only neutral expression. We perform experimental investigation to evaluate the significance of different facial regions in the task of gender classification. Three most significant regions are used in our fusion-based method. The classification is performed by using support vector machines based on the features extracted using two-dimension principal component analysis. Experiments show that our fusion-based method is able to compensate for facial expressions and obtained the highest correct classification rate of 95.33%.

Fusion of Multiple Facial Features for Age Estimation

A novel age estimation method is presented which improves performance by fusing complementary information acquired from global and local features of the face. Two-directional two-dimensional principal component analysis ((2D)2PCA) is used for dimensionality reduction and construction of individual feature spaces. Each feature space contributes a confidence value which is calculated by Support vector machines (SVMs). The confidence values of all the facial features are then fused for final age estimation. Experimental results demonstrate that fusing multiple facial features can achieve significant accuracy gains over any single feature. Finally, we propose a fusion method that further improves accuracy.

Two-Dimensional Dynamic Principal Component Analysis with Autodetermined Support Region

Dynamics are inherent characteristics of batch processes. In some cases, such dynamics exist not only within a particular batch, but also from batch to batch. In previous work, two-dimensional dyna...

Image denoising by using nonseparable wavelet filters and two-dimensional principal component analysis

In this paper, we propose an image denoising method based on nonseparable wavelet filter banks and two-dimensional principal component analysis (2D-PCA). Conventional wavelet domain processing techniques are based on modifying the coefficients of separable wavelet transform of an image. In general, separable wavelet filters have limited capability of capturing the directional information. In contrast, nonseparable wavelet filters contain the basis elements oriented at a variety of directions and different filter banks capture the different directional features of an image. Furthermore, we identify the patterns from the noisy image by using the 2D-PCA. In comparison to the prevalent denoising algorithms, our proposed algorithm features no complex preprocessing. Furthermore, we can adjust the wavelet coefficients by a threshold according to the denoising results. We apply our proposed technique to some benchmark images with white noise. Experimental results show that our new technique achieves both good visual quality and a high peak signal-to-noise ratio for the denoised images.

A simplified GLRAM algorithm for face recognition

In this paper we propose a new face recognition method based on the generalized low rank approximations of matrices (GLRAM). First, we investigate the GLRAM and its associated coupled subspace analysis and then propose a new simplified algorithm, which is named as SGLRAM aiming at deriving the projection matrices for GLRAM. We implement all these algorithms (GLRAM SGLRAM) for face recognition on the ORL and YaleB databases and the experiments show that the SGLRAM can produce comparable high performance compared to the approached of two-dimensional principal component analysis (2DPCA) and GLRAM. However, it will cost much less time than the GLRAM in training and save more space than the 2DPCA in testing.

Binary Two-Dimensional PCA

Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.

Symmetry based two-dimensional principal component analysis and its application to face recognition

This paper presented a Symmetry based Two-Dimensional Principal Component Analysis (STDPCA) on the basis of this idea of symmetry, which was introduced into Two-Dimensional Principal Component Analysis (TDPCA). Firstly, facial image was divided into the even symmetrical image and the odd symmetrical image. Then TDPCA was performed in the even symmetrical image and the odd symmetrical image for feature extraction, respectively. Therefore, STDPCA used effectively not only the advantages of TDPCA, but also the symmetrical properties of facial images. The experimental results on ORL and YALE database show the efficiency of STDPCA.

Eigendetection of masses considering false positive reduction and breast density information

The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.

Subspace identification for two-dimensional dynamic batch process statistical monitoring

Dynamics are inherent characteristics of batch processes, which may be not only within a batch, but also from batch to batch. Two-dimensional dynamic principal component analysis (2-D-DPCA) method [Lu, N., Yao, Y., Gao, F., 2005. Two-dimensional dynamic PCA for batch process monitoring. A.I.Ch.E. Journal 51, 3300–3304] can model both kinds of batch dynamics, but may lead to the inclusion of large number of lagged variables and make the contribution plot difficult to read. To solve this problem, subspace identification technique is combined with 2-D-DPCA in this paper. The state space model of a 2-D batch process can be identified with canonical variate analysis (CVA) method based on the auto-determined support region (ROS). In 2-D-DPCA modeling, the utilization of state variables instead of lagged process variables reduces the number of variables and provides a clearer contribution plot for fault diagnosis.

Two-directional maximum scatter difference discriminant analysis for face recognition

In this paper, we propose a novel method for image feature extraction. This method combines the ideas of two-dimensional principal component analysis and two-dimensional maximum scatter difference and which can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the scatter difference criterion. The proposed method not only avoids the singularity problem frequently occurred in the classical Fisher discriminant analysis due to the small sample size, but also saves much computational time. In addition, the proposed method can simultaneously make use of the discriminant information and descriptive information of the image. Experiments conducted on FERET, and ORL face databases demonstrate the effectiveness of the proposed method.

A THEORETICAL FRAMEWORK FOR MATRIX-BASED FEATURE EXTRACTION ALGORITHMS WITH ITS APPLICATION TO IMAGE RECOGNITION

Recently proposed matrix-based methods, two-dimensional Principal Component Analysis (2DPCA), two-dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Locality Preserving Projections (2DLPP) have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-based methods. In this paper, we propose a general theoretical framework for matrix-based feature extraction algorithms from the point of view of graph embedding. Our framework can be applied to extend two recently proposed vector-based algorithms, i.e. Unsupervised Discriminant Projection (UDP) and Marginal Fisher Analysis (MFA) algorithms, to their matrix-based versions. Further, our framework can also be used as a platform to generate new matrix-based feature extraction algorithms by designing meaningful graphs, e.g. two-dimensional Discriminant Embedding Analysis (2DDEA) in this paper. It is shown that 2DLDA is actually a special case of the 2DDEA method. Experiments on three publicly available image databases demonstrate the effectiveness of the proposed algorithm. Our results fit into the scene for a better picture about the matrix-based feature extraction algorithms.

Image-based facial recognition in the domain of high-order polynomial one-way mapping

The authors present a secure facial recognition system. The biometric data are transformed to the cancellable domain using high-order polynomial functions and co-occurrence matrices. The proposed method has provided both high-recognition accuracy and biometric data protection. Protection of data relies on the polynomial functions, where the new reissued cancellable biometric can be obtained by changing the polynomial parameters. Besides the protection of data, the reconstructed co-occurrence matrices also contributed to the accuracy enhancement. Hadamard product is used to reconstruct the new measure and has shown high flexibility in proving a new relationship between two independent covariance matrices. The proposed cancellable biometric is treated in the same manner as the original biometric data, which enables replacement of original data by the novel cancellable algorithm with no change to the authentication system. The two-dimensional principal component analysis recognition algorithm is used at the authentication stage. Results have shown high non-reversibility of data with improved accuracy over the original data and raised the performance recognition rate to 97%.

Batch Process Monitoring in Score Space of Two-Dimensional Dynamic Principal Component Analysis (PCA)

Two-dimensional dynamic principal component analysis (2-D-DPCA) is a recent developed method for two-dimensional (2-D) dynamic batch process monitoring. However, it only utilizes residual information in fault detection and information in score space is wasted, which may compromise the monitoring efficiency. In this paper, 2-D multivariate score autoregressive (AR) filters are designed to remove the 2-D dynamics retained in score space and make the filtered scores obey certain statistical assumptions, so that the T2 statistic can be calculated reasonably for process monitoring. Simulation shows that using the filters enhances the monitoring efficiency while reducing the chances of false alarms and missed alarms.