Discriminant clustering embedding for face recognition with image sets

Authors:
Youdong Zhao;Shuang Xu;Yunde Jia
Affiliations:
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P.R. China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P.R. China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, P.R. China
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Year:
2007

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Abstract

In this paper, a novel local discriminant embedding method, Discriminant Clustering Embedding (DCE), is proposed for face recognition with image sets. DCE combines the effectiveness of submanifolds, which are extracted by clustering for each subject's image set, characterizing the inherent structure of face appearance manifold and the discriminant property of discriminant embedding. The low-dimensional embedding is learned via preserving the neighbor information within each submanifold, and separating the neighbor submanifolds belonging to different subjects from each other. Compared with previous work, the proposed method could not only discover the most powerful discriminative information embedded in the local structure of face appearance manifolds more sufficiently but also preserve it more efficiently. Extensive experiments on real world data demonstrate that DCE is efficient and robust for face recognition with image sets.