Daiyun Luo

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Automatic extraction of fiducial facial points is one of the key steps to face tracking, recognition, and animation. Great facial variations, especially pose or viewpoint changes, typically degrade the performance of classical methods. Recent learning or regression-based approaches highly rely on the availability of a training set that covers facial(More)
Great attention has been devoted to the development of shape descriptors that is the key to object recognition. Previous works have great success on either relatively simple shapes or limited transformations, e.g., translation, rotation and scaling. We propose a new projective invariant, named characteristic number (CN) that includes more points for complex(More)
Geometric invariants are important for shape recognition and matching. Existing invariants in projective geometry are typically defined on the limited number (e.g., five for the classical cross-ratio) of collinear planar points and also lack the ability to characterize the curve or surface underlying the given points. In this paper, we present a projective(More)
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