Results 11 - 20
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51
Multilinear Subspace Learning for Face and Gait Recognition
, 2008
"... Face and gait recognition problems are challenging due to largely varying appear-ances, highly complex pattern distributions, and insufficient training samples. This dis-sertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learne ..."
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Face and gait recognition problems are challenging due to largely varying appear-ances, highly complex pattern distributions, and insufficient training samples. This dis-sertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for sys-tematic treatment of the multilinear subspace learning problem. Three multilinear pro-jections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear prin-cipal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear dis-criminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated mul-
Entropic Affinities: Properties and Efficient Numerical Computation
"... Gaussian affinities are commonly used in graph-based methods such as spectral clustering or nonlinear embedding. Hinton and Roweis (2003) introduced a way to set the scale individually for each point so that it has a distribution over neighbors with a desired perplexity, or effective number of neigh ..."
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Gaussian affinities are commonly used in graph-based methods such as spectral clustering or nonlinear embedding. Hinton and Roweis (2003) introduced a way to set the scale individually for each point so that it has a distribution over neighbors with a desired perplexity, or effective number of neighbors. This gives very good affinities that adapt locally to the data but are harder to compute. We study the mathematical properties of these “entropic affinities ” and show that they implicitly define a continuously differentiable function in the input space and give bounds for it. We then devise a fast algorithm to compute the widths and affinities, based on robustified, quickly convergent root-finding methods combined with a treeor density-based initialization scheme that exploits the slowly-varying behavior of this function. This algorithm is nearly optimal andmuchmoreaccurateandfastthantheexisting bisection-based approach, particularly with large datasets, as we show with image and text data. Many machine learning algorithms rely on the choice of meta-parameters that govern their performance. These parameters depend on the data and good values are often hard to find. One such meta-parameter is the bandwidth σ that is used in the construction of affinities in many machine learning problems. These include dimensionality reduction methods such as LLE (Roweis & Saul, 2000), Laplacian eigenmaps (Belkin & Niyogi, 2003),
Comparative Analysis of Face Recognition Approaches: A Survey
"... In recent days, the need of biometric security system is heightened for providing safety and security against terrorist attacks, robbery, etc. The demand of biometric system has risen due to its strength, efficiency and easy availability. One of the most effective, highly authenticated and easily ad ..."
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In recent days, the need of biometric security system is heightened for providing safety and security against terrorist attacks, robbery, etc. The demand of biometric system has risen due to its strength, efficiency and easy availability. One of the most effective, highly authenticated and easily adaptable biometric security systems is facial feature recognition. This paper h a s covered almost all the techniques for face recognition approaches. It also covers the relative analysis between all the approaches which are useful in face recognition. Consideration of merits and demerits of all techniques is done and recognition rates of all the techniques are also compared.
A Discriminant Pseudo Zernike Moments in Face Recognition
"... This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher’s Linear Discriminant to achieve a stable numerical computation and good generalization in smal ..."
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This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher’s Linear Discriminant to achieve a stable numerical computation and good generalization in small-sample-size problems. Fisher’s Linear Discriminant uses pseudo Zernike moments to derive an enhanced subset of moment features by maximizing the between-class scatter, while minimizing the within-class scatter, which leads to a better discrimination and classification performance. Experimental results show that the proposed method achieves superior performance with a recognition rate of 97.51 % in noise free environment and 97.12 % in noise induced environment for the Essex Face94 database. For the Essex Face95 database, the recognition rates obtained are 91.73 % and 90.30 % in noise free and noise induced environments, respectively. ACM Classification: I.5.4. (Computer Methodologies-Pattern Recognition – Applications) 1.
Image Pixel Fusion for Human Face Recognition”, appeared
- in International Journal of Recent Trends in Engineering [ISSN 1797-9617], published by Academy Publishers
, 2009
"... Abstract—In this paper we present a technique for fusion of optical and thermal face images based on image pixel fusion approach. Out of several factors, which affect face recognition performance in case of visual images, illumination changes are a significant factor that needs to be addressed. Ther ..."
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Abstract—In this paper we present a technique for fusion of optical and thermal face images based on image pixel fusion approach. Out of several factors, which affect face recognition performance in case of visual images, illumination changes are a significant factor that needs to be addressed. Thermal images are better in handling illumination conditions but not very consistent in capturing texture details of the faces. Other factors like sunglasses, beard, moustache etc also play active role in adding complicacies to the recognition process. Fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Here fused images are projected into an eigenspace and the projected images are classified using a radial basis function (RBF) neural network and also by a multi-layer perceptron (MLP). In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Comparison of experimental results show that the proposed approach performs significantly well in recognizing face images with a success rate of 96 % and 95.07 % for RBF Neural Network and MLP respectively.
A New Face Recognition Method using PCA, LDA and Neural Network
"... Abstract—In this paper, a new face recognition method based on ..."
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Abstract—In this paper, a new face recognition method based on
T.: Multivariate Pattern Classification based on Local Discriminant Component Analisys
- Proc. of IEEE International Conference on Robotics and Biomimetics (2004) Paper-ID: 290 174
"... Abstract — This paper proposes a novel local discriminant component analysis (DCA) algorithm that is useful for pattern classification of high-dimensional data. Different from most traditional methods, in which feature extractors are usually used prior to a classifier, the proposed method incorporat ..."
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Abstract — This paper proposes a novel local discriminant component analysis (DCA) algorithm that is useful for pattern classification of high-dimensional data. Different from most traditional methods, in which feature extractors are usually used prior to a classifier, the proposed method incorporates the feature extraction process into the classifier. Then, a probabilistic neural network is developed based on the idea of local DCA, in which the whole network including the feature extractor and the classifier can be modulated according to a single training criterion, so that features suited to the classification purpose can be extracted. In this paper, a hybrid training algorithm is proposed on the basis of the minimum classification error (MCE) learning. In simulation experiments, benchmark data are used to prove feasibility of the proposed method. Keywords-Gaussian mixture model; orthogonal transforma-tions; multivariate analysis; discriminant component analysis I.
A Supervised Clustering Algorithm for the Initialization of RBF Neural Network Classifiers
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"... Abstract—This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant A ..."
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Abstract—This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%. Keywords—Face recognition, linear discriminant analysis, radial basis function network. I.
PARAMETER INTERVAL SYSTEMS
, 2004
"... A multivariable control technique is proposed for a type of nonlinear system with parameter intervals. The control is based upon the feedback linearization scheme called Generic Model Control, and alters the control calculation by utilizing parameter intervals, employing an adaptive step, averaging ..."
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A multivariable control technique is proposed for a type of nonlinear system with parameter intervals. The control is based upon the feedback linearization scheme called Generic Model Control, and alters the control calculation by utilizing parameter intervals, employing an adaptive step, averaging control predictions, and applying an interval problem solution. The proposed approach is applied in controlling both a linear and a nonlinear arc
