The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embedding model, called semi-supervised graph embedding (SGE). This algorithm builds an adjacency graph which can best respect the geometry structure inferred from the must-link pairwise...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features. In this paper, a new manifold learning algorithm, called Uncorrelated Locality Information Projection (ULIP), to identify the underlying manifold structure of a data set. ULIP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently,...
Automatic face detection and tracking system based on the new time-variant weighted Hinfin filter is presented in this paper. The proposed system is used to track and predict the location of a moving personpsilas face in different background. Dealing with the deficient knowledge of the system model and uncertainties, Hinfin that possesses the characteristic of minimizing the worst-case estimation-error...
In this paper, based on face recognition, the core algorithm is studied, which was applied in access control system. This algorithm carries on two-dimensional principal component analysis (2DPCA), singular value decomposition (SVD) and the fusion of 2DPCA and SVD, which the main characteristic vector of the PCA is fused to form a new one that is taken as the distinction standard. The experiment results,...
Nowadays the network information security is an important research direction on data communications. In order to solve a number of shortcomings in information security password system, in this paper, a new criterion based on image segmentation on face recognition is applied to network security. Firstly, in proposed approach, the original images are divided into modular images, which are also called...
The kernel fisher nonlinear discriminant analysis (KFDA) has become one of the most effective methods applied to extract the nonlinear discriminant face features. However, the face recognition problem is a typical problem of high dimension with small sample, the KFDA is imperfect because the within-class scatter matrix is irreversible. In this paper, a new method (L-KFDA) is proposed to extract the...
During recent years a special class of nonlinear dimensionality reduction (NLDR) methods known as manifold learning methods, obtain a lot of attention for low dimension representation of high dimensional data. Most commonly used NLDR methods like Isomap, locally linear embedding, local tangent space alignment, Hessian locally linear embedding, Laplacian eigenmaps and diffusion maps, construct their...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.