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.
A linear regression-based method is a hot topic in face recognition community. Recently, sparse representation and collaborative representation-based classifiers for face recognition have been proposed and attracted great attention. However, most of the existing regression analysis-based methods are sensitive to pose variations. In this paper, we introduce the orthogonal Procrustes problem (OPP) as...
In this paper we propose a new linear feature extraction approach called Weighted Linear Embedding (WLE). WLE combines Fisher criterion with manifold learning criterion like local discriminant embedding analysis (LDE), whereas unlike LDE that only utilizes local neighbor information it uses local information and nonlocal information simultaneously. WLE is also unlike linear discriminant analysis (LDA)...
In this paper, we propose a novel supervised learning method called Global Sparse Representation Projections (GSRP) for linear dimensionality reduction. GSRP can be viewed as a combiner of sparse representation and manifold learning. But differing from the recent manifold learning methods such as Local Preserving Projections (LPP), GSRP introduces the global sparse representation information into...
In this paper, a novel discriminant feature extraction algorithm employing center-based distance is proposed for face recognition. This new method, which is a supervised linear dimensionality reduction and feature extraction approach, computes the center-based distance between each training sample-pairs in the same class and the distance between each training sample-pair belonging to different classes...
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.