Face recognition across pose: A review
Pattern Recognition
Generic facial encoding for shape alignment with active models
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. One strategy is to synthesize virtual face views from real views. In this paper, a novel 3D face shape-modeling algorithm, Multilevel Quadratic Variation Minimization (MQVM), is proposed. Our method makes sole use of two orthogonal real views of a face, i.e., the frontal and profile views. By applying quadratic variation minimization iteratively in a coarse-to-fine hierarchy of control lattices, the MQVM algorithm can generate C虏-smooth 3D face surfaces. Then realistic virtual face views can be synthesized by rotating the 3D models. The algorithm works properly on sparse constraint points and large images. It is much more efficient than single-level quadratic variation minimization. The modeling results suggest the validity of the MQVM algorithm for 3D face modeling and 2D face view synthesis under different poses.