Abstract. Face recognition with single sample per person (SSPP) is a very challenging task because in such a scenario it is difficult to predict the facial variations of a query sample by the gallery samples. Considering the fact that different parts of human faces have different importance to face recognition, and the fact that the intra-class facial variations can be shared across different subjects, we propose a local generic representation (LGR) based framework for face recognition with SSPP. A local gallery dictionary is built by extracting the neighboring patches from the gallery dataset, while an intra-class variation dictionary is built by using an external generic dataset to predict the possible facial variations (e.g., illuminations, pose, expressions and disguises).LGR minimizes the total representation residual of the query sample over the local gallery dictionary and the generic variation dictionary, and it uses correntropy to measure the representation residual of each patch. Half-quadratic analysis is adopted to solve the optimization problem.LGR takes the advantages of patch based local representation and generic variation representation, showing leading performance in face recognition with SSPP.
Abstract. In this paper, we define a conditional transform with respect to the Gaussian process, the conditional convolution product and the first variation of functionals via the Gaussian process. We then examine various relationships of the conditional transform with respect to the Gaussian process, the conditional convolution product and the first variation for functionals F in Sα [5,8].
In this paper, we define the generalized conditional transform with respect to the Gaussian process. We then establish the integration formulas for the generalized conditional transform with respect to the Gaussian process in terms of the generalized conditional -product and the first variation. Also, we show that the generalized conditional transform with respect to the Gaussian process for the first variation of F can be expressed in terms of the ordinary function space integral of F multiplied by a linear factor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.