Abstract
In this paper, we propose a constrained hybrid optimization algorithm that incorporates several shape constraints into a gradient descent procedure using a novel unbiased cost function. Shape constraints are heuristically derived from face images where the face shape can be directly estimated based on ”motion” analysis. To better locate face contour points regardless of the background, local projection models are used. Experiments show that our algorithm benefits significantly from these shape constraints and achieves a much higher convergent rate compared to the inverse compositional optimization algorithm. We test our algorithm on different face databases, and demonstrate its robustness in presence of various illuminations, background patterns, as well as variations in face expressions.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, C., Cohen, F.S. (2005). A Constrained Hybrid Optimization Algorithm for Morphable Appearance Models. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_37
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DOI: https://doi.org/10.1007/11585978_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30287-2
Online ISBN: 978-3-540-32098-2
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