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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    Title :

    Constructing facial identity surfaces in a nonlinear discriminating space


    Contributors:
    Yongmin Li, (author) / Shaogang Gong, (author) / Liddell, H. (author)


    Publication date :

    2001-01-01


    Size :

    693023 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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