IEEE Trans Image Process - Manifold-manifold distance and its application to face recognition with image sets.

Tópicos

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Resumo

In this paper, we address the problem of classifying image sets for face recognition, where each set contains images belonging to the same subject and typically covering large variations. By modeling each image set as a manifold, we formulate the problem as the computation of the distance between two manifolds, called manifold-manifold distance (MMD). Since an image set can come in three pattern levels, point, subspace, and manifold, we systematically study the distance among the three levels and formulate them in a general multilevel MMD framework. Specifically, we express a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrate the distances between pairs of subspaces from one of the involved manifolds. We theoretically and experimentally study several configurations of the ingredients of MMD. The proposed method is applied to the task of face recognition with image sets, where identification is achieved by seeking the minimum MMD from the probe to the gallery of image sets. Our experiments demonstrate that, as a general set similarity measure, MMD consistently outperforms other competing nondiscriminative methods and is also promisingly comparable to the state-of-the-art discriminative methods.

Resumo Limpo

paper address problem classifi imag set face recognit set contain imag belong subject typic cover larg variat model imag set manifold formul problem comput distanc two manifold call manifoldmanifold distanc mmd sinc imag set can come three pattern level point subspac manifold systemat studi distanc among three level formul general multilevel mmd framework specif express manifold collect local linear model depict subspac mmd convert integr distanc pair subspac one involv manifold theoret experiment studi sever configur ingredi mmd propos method appli task face recognit imag set identif achiev seek minimum mmd probe galleri imag set experi demonstr general set similar measur mmd consist outperform compet nondiscrimin method also promis compar stateoftheart discrimin method

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