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.


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    Titel :

    Discriminant Clustering Embedding for Face Recognition with Image Sets


    Beteiligte:
    Yagi, Yasushi (Herausgeber:in) / Kang, Sing Bing (Herausgeber:in) / Kweon, In So (Herausgeber:in) / Zha, Hongbin (Herausgeber:in) / Zhao, Youdong (Autor:in) / Xu, Shuang (Autor:in) / Jia, Yunde (Autor:in)

    Kongress:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Erschienen in:

    Computer Vision – ACCV 2007 ; Kapitel : 63 ; 641-650


    Erscheinungsdatum :

    2007-01-01


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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