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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-6
Christoph Mayer , Image Understanding Group, Technische Universitát München, Garching, Germany
Freek Stulp , Cognitive Neuroinformatics Group, University of Bremen, Germany
Matthias Wimmer , Perceptual Computing Lab, Waseda University, Tokyo, Japan
Geometric models allow to determine semantic information about real-world objects. Model fitting algorithms need to find the best match between a parameterized model and a given image. This task inherently requires an objective function to estimate the error between a model parameterization and an image. The accuracy of this function directly influences the accuracy of the entire process of model fitting. Unfortunately, building these functions is a non-trivial task. Dedicated to the application of face model fitting, this paper proposes to consider a multi-band image representation that indicates the facial components, from which a large set of image features is computed. Since it is not possible to manually formulate an objective function that considers this large amount of features, we apply a Machine Learning framework to construct them. This automatic approach is capable of considering the large amount of features provided and yield highly accurate objective functions for face model fitting. Since the Machine Learning framework rejects non-relevant image features, we obtain high performance runtime characteristics as well.
Christoph Mayer, Freek Stulp, Matthias Wimmer, "Face model fitting based on machine learning from multi-band images of facial components", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-6, doi:10.1109/CVPRW.2008.4563086
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