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1.
Block-wise 2D kernel PCA/LDA for face recognition 总被引:1,自引:0,他引:1
Armin Eftekhari Hamid Abrishami Moghaddam Javad Alirezaie 《Information Processing Letters》2010,110(17):761-766
Direct extension of (2D) matrix-based linear subspace algorithms to kernel-induced feature space is computationally intractable and also fails to exploit local characteristics of input data. In this letter, we develop a 2D generalized framework which integrates the concept of kernel machines with 2D principal component analysis (PCA) and 2D linear discriminant analysis (LDA). In order to remedy the mentioned drawbacks, we propose a block-wise approach based on the assumption that data is multi-modally distributed in so-called block manifolds. Proposed methods, namely block-wise 2D kernel PCA (B2D-KPCA) and block-wise 2D generalized discriminant analysis (B2D-GDA), attempt to find local nonlinear subspace projections in each block manifold or alternatively search for linear subspace projections in kernel space associated with each blockset. Experimental results on ORL face database attests to the reliability of the proposed block-wise approach compared with related published methods. 相似文献
2.
Sang-Il Choi Author Vitae Chunghoon Kim Author Vitae Author Vitae 《Pattern recognition》2007,40(7):2118-2125
Illumination variation that occurs on face images degrades the performance of face recognition. In this paper, we propose a novel approach to handling illumination variation for face recognition. Since most human faces are similar in shape, we can find the shadow characteristics, which the illumination variation makes on the faces depending on the direction of light. By using these characteristics, we can compensate for the illumination variation on face images. The proposed method is simple and requires much less computational effort than the other methods based on 3D models, and at the same time, provides a comparable recognition rate. 相似文献
3.
In this paper, we present an efficient algorithm to solve the most discriminant vectors of LDA for high-dimensional data set. The experiments on ORL face database confirm the effectiveness of the proposed method. 相似文献
4.
This paper proposes a new subspace method that is based on image covariance obtained from windowed features of images. A windowed input feature consists of a number of pixels, and the dimension of input space is determined by the number of windowed features. Each element of an image covariance matrix can be obtained from the inner product of two windowed features. The 2D-PCA and 2D-LDA methods are then obtained from principal component analysis and linear discriminant analysis, respectively, using the image covariance matrix. In the case of 2D-LDA, there is no need for PCA preprocessing and the dimension of subspace can be greater than the number of classes because the within-class and between-class image covariance matrices have full ranks. Comparative experiments are performed using the FERET, CMU, and ORL databases of facial images. The experimental results show that the proposed 2D-LDA provides the best recognition rate among several subspace methods in all of the tests. 相似文献
5.
Neighborhood preserving embedding (NPE) is a linear approximation to the locally linear embedding algorithm which can preserve the local neighborhood structure on the data manifold. However, in typical face recognition where the number of data samples is smaller than the dimension of data space, it is difficult to directly apply NPE to high dimensional matrices because of computational complexity. Moreover, in such case, NPE often suffers from the singularity problem of eigenmatrix, which makes the direct implementation of the NPE algorithm almost impossible. In practice, principal component analysis or singular value decomposition is applied as a preprocessing step to attack these problems. Nevertheless, this strategy may discard dimensions that contain important discriminative information and the eigensystem computation of NPE could be unstable. Towards a practical dimensionality reduction method for face data, we develop a new scheme in this paper, namely, the complete neighborhood preserving embedding (CNPE). CNPE transforms the singular generalized eigensystem computation of NPE into two eigenvalue decomposition problems. Moreover, a feasible and effective procedure is proposed to alleviate the computational burden of high dimensional matrix for typical face image data. Experimental results on the ORL face database and the Yale face database show that the proposed CNPE algorithm achieves better performance than other feature extraction methods, such as Eigenfaces, Fisherfaces and NPE, etc. 相似文献
6.
Xiao-Sheng Zhuang Author Vitae Author Vitae 《Pattern recognition》2007,40(5):1570-1578
Many pattern recognition applications involve the treatment of high-dimensional data and the small sample size problem. Principal component analysis (PCA) is a common used dimension reduction technique. Linear discriminate analysis (LDA) is often employed for classification. PCA plus LDA is a famous framework for discriminant analysis in high-dimensional space and singular cases. In this paper, we examine the theory of this framework and find out that even if there is no small sample size problem the PCA dimension reduction cannot guarantee the subsequent successful application of LDA. We thus develop an improved discriminate analysis method by introducing an inverse Fisher criterion and adding a constrain in PCA procedure so that the singularity phenomenon will not occur. Experiment results on face recognition suggest that this new approach works well and can be applied even when the number of training samples is one per class. 相似文献
7.
NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear
discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines
the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into
a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental
results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and
a comparable performance with SVM but costing less computational time.
Danian Zheng received his Bachelor degree in Computer Science and Technology in 2002 from Tsinghua University, Beijing, China. He received his Master degree and Doctoral degree in Computer Science and Technology in 2006 from Tsinghua University. He is currently a researcher in Fujitsu R&D Center Co. Ltd, Beijing, China. His research interests are mainly in the areas of support vector machines, kernel methods and their applications. Meng Na received her Bachelor degree in Computer Science and Technology in 2003 from Northeastern, China. Since 2003 she has been pursuing the Master degree and the Doctoral degree at the Department of Computer Science and Technology at Tsinghua University. Her research interests are in the area of image processing, pattern recognition, and virtual human. Jiaxin Wang received his Bachelor degree in Automatic Control in 1965 from Beijing University of Aeronautics and Astronautics, his Master degree in Computer Science and Technology in 1981 from Tsinghua University, Beijing, China, and his Doctoral degree in 1996 from Engineering Faculty of Vrije Universiteit Brussel, Belgium. He is currently a professor of Department of Computer Science and Technology, Tsinghua University. His research interests are in the areas of artificial intelligence, intelligent control and robotics, machine learning, pattern recognition, image processing and virtual reality. 相似文献
Jiaxin Wang (Corresponding author)Email: |
Danian Zheng received his Bachelor degree in Computer Science and Technology in 2002 from Tsinghua University, Beijing, China. He received his Master degree and Doctoral degree in Computer Science and Technology in 2006 from Tsinghua University. He is currently a researcher in Fujitsu R&D Center Co. Ltd, Beijing, China. His research interests are mainly in the areas of support vector machines, kernel methods and their applications. Meng Na received her Bachelor degree in Computer Science and Technology in 2003 from Northeastern, China. Since 2003 she has been pursuing the Master degree and the Doctoral degree at the Department of Computer Science and Technology at Tsinghua University. Her research interests are in the area of image processing, pattern recognition, and virtual human. Jiaxin Wang received his Bachelor degree in Automatic Control in 1965 from Beijing University of Aeronautics and Astronautics, his Master degree in Computer Science and Technology in 1981 from Tsinghua University, Beijing, China, and his Doctoral degree in 1996 from Engineering Faculty of Vrije Universiteit Brussel, Belgium. He is currently a professor of Department of Computer Science and Technology, Tsinghua University. His research interests are in the areas of artificial intelligence, intelligent control and robotics, machine learning, pattern recognition, image processing and virtual reality. 相似文献
8.
This paper presents a modified bacterial foraging optimization algorithm called adaptive crossover bacterial foraging optimization algorithm (ACBFOA), which incorporates adaptive chemotaxis and also inherits the crossover mechanism of genetic algorithm. First part of the research work aims at improvising evaluation of the optimal objective function values. The idea of using adaptive chemotaxis is to make it computationally efficient and crossover technique is to search nearby locations by offspring bacteria. Four different benchmark functions are considered for performance evaluation. The purpose of this research work is also to investigate a face recognition algorithm with improved recognition rate. In this connection, we propose a new algorithm called ACBFO-Fisher. The proposed ACBFOA is used for finding optimal principal components for dimension reduction in linear discriminant analysis (LDA) based face recognition. Three well-known face databases, FERET, YALE and UMIST, are considered for validation. A comparison with the results of earlier methods is presented to reveal the effectiveness of the proposed ACBFO-Fisher algorithm. 相似文献
9.
Although many algorithms have been proposed, face recognition and verification systems can guarantee a good level of performances only for controlled environments. In order to improve the performance and robustness of face recognition and verification systems, multi-modal and mono-modal systems based on the fusion of multiple recognisers using different or similar biometrics have been proposed, especially for verification purposes. In this paper, a recognition and verification system based on the combination of two well-known appearance-based representations of the face, namely, principal component analysis (PCA) and linear discriminant analysis (LDA), is proposed. Both PCA and LDA are used as feature extractors from frontal view images. The benefits of such a fusion are shown for different environmental conditions, namely, ideal conditions, characterised by a very limited variability of environmental parameters, and real conditions with a large variability of lighting, scale and facial expression. 相似文献
10.
Weihong Deng Author Vitae Jiani Hu Author Vitae Author Vitae Weidong Cai Author Vitae Author Vitae 《Pattern recognition》2010,43(5):1748-1762
Current face recognition techniques rely heavily on the large size and representativeness of the training sets, and most methods suffer degraded performance or fail to work if there is only one training sample per person available. This so-called “one sample problem” is a challenging issue in face recognition. In this paper, we propose a novel feature extraction method named uniform pursuit to address the one sample problem. The underlying idea is that most recognition errors are due to the confusions between faces that look very similar, and thus one can reduce the risk of recognition error by mapping the close class prototypes to be distant, i.e., uniforming the pairwise distances between different class prototypes. Specifically, the UP method pursues, in the whitened PCA space, the low dimensional projections that reduce the local confusion between the similar faces. The resulting low dimensional transformed features are robust against the complex image variations such as those caused by lighting and aging. A standardized procedure on the large-scale FERET and FRGC databases is applied to evaluate the one sample problem. Experimental results show that the robustness, accuracy and efficiency of the proposed UP method compare favorably to the state-of-the-art one sample based methods. 相似文献
11.
This paper presents a new scheme of face image feature extraction, namely, the two-dimensional Fisher linear discriminant. Experiments on the ORL and the UMIST face databases show that the new scheme outperforms the PCA and the conventional PCA+FLD schemes, not only in its computational efficiency, but also in its performance for the task of face recognition. 相似文献
12.
This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA + IDA and ACS–IDA. Interestingly, PCA + IDA offers us a perturbation free algorithm for dimension reduction while ACS + IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases—YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm. 相似文献
13.
Regularized discriminant analysis for the small sample size problem in face recognition 总被引:1,自引:0,他引:1
It is well-known that the applicability of both linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) to high-dimensional pattern classification tasks such as face recognition (FR) often suffers from the so-called “small sample size” (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. In this paper, we propose a new QDA like method that effectively addresses the SSS problem using a regularization technique. Extensive experimentation performed on the FERET database indicates that the proposed methodology outperforms traditional methods such as Eigenfaces, direct QDA and direct LDA in a number of SSS setting scenarios. 相似文献
14.
15.
In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination. 相似文献
16.
This paper formulates independent component analysis (ICA) in the kernel-inducing feature space and develops a two-phase kernel ICA algorithm: whitened kernel principal component analysis (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The experiment using a subset of FERET database indicates that the proposed kernel ICA method significantly outperform ICA, PCA and KPCA in terms of the total recognition rate. 相似文献
17.
Ping-Cheng Hsieh Author Vitae Author Vitae 《Pattern recognition》2009,42(5):978-984
Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods. 相似文献
18.
Itzik Pima 《Pattern recognition》2004,37(9):1945-1948
This paper studies regularized discriminant analysis (RDA) in the context of face recognition. We check RDA sensitivity to different photometric preprocessing methods and compare its performance to other classifiers. Our study shows that RDA is better able to extract the relevant discriminatory information from training data than the other classifiers tested, thus obtaining a lower error rate. Moreover, RDA is robust under various lighting conditions while the other classifiers perform badly when no photometric method is applied. 相似文献
19.
Xiao-ning Song Yu-jie Zheng Xiao-jun Wu Xi-bei Yang Jing-yu Yang 《Applied Soft Computing》2010,10(1):208-214
In this paper, some studies have been made on the essence of fuzzy linear discriminant analysis (F-LDA) algorithm and fuzzy support vector machine (FSVM) classifier, respectively. As a kernel-based learning machine, FSVM is represented with the fuzzy membership function while realizing the same classification results with that of the conventional pair-wise classification. It outperforms other learning machines especially when unclassifiable regions still remain in those conventional classifiers. However, a serious drawback of FSVM is that the computation requirement increases rapidly with the increase of the number of classes and training sample size. To address this problem, an improved FSVM method that combines the advantages of FSVM and decision tree, called DT-FSVM, is proposed firstly. Furthermore, in the process of feature extraction, a reformative F-LDA algorithm based on the fuzzy k-nearest neighbors (FKNN) is implemented to achieve the distribution information of each original sample represented with fuzzy membership grade, which is incorporated into the redefinition of the scatter matrices. In particular, considering the fact that the outlier samples in the patterns may have some adverse influence on the classification result, we developed a novel F-LDA algorithm using a relaxed normalized condition in the definition of fuzzy membership function. Thus, the classification limitation from the outlier samples is effectively alleviated. Finally, by making full use of the fuzzy set theory, a complete F-LDA (CF-LDA) framework is developed by combining the reformative F-LDA (RF-LDA) feature extraction method and DT-FSVM classifier. This hybrid fuzzy algorithm is applied to the face recognition problem, extensive experimental studies conducted on the ORL and NUST603 face images databases demonstrate the effectiveness of the proposed algorithm. 相似文献