Recognising face with large pose variation is more challenging than that in a fixed view, e.g. frontal-view, due to the severe non-linearity caused by rotation in depth, self-shading and self-occlusion. To address this problem, a multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns from multi-view face images. Kernel Discriminant Analysis is developed to extract the significant non-linear discriminating features which maximise the between-class variance and minimise the within-class variance. By using the kernel technique, this process is equivalent to a Linear Discriminant Analysis in a high-dimensional feature space which can be solved conveniently. The identity surfaces are then constructed from these non-linear discriminating features. Face recognition can be performed dynamically from an image sequence by matching an object trajectory and model trajectories on the identity surfaces.


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    Titel :

    Constructing facial identity surfaces in a nonlinear discriminating space


    Beteiligte:
    Yongmin Li, (Autor:in) / Shaogang Gong, (Autor:in) / Liddell, H. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    693023 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Li, Y. / Gong, S. / Liddell, H. et al. | British Library Conference Proceedings | 2001


    Constructing Facial Identity Surfaces for Recognition

    Li, Y. / Gong, S. / Liddell, H. | British Library Online Contents | 2003


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