Year: 2015
Type: conference-paper
Abstract: In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable Face Model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on Morphable Model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a 3D Morphable Model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library at github.com/patrikhuber/superviseddescent.
Authors: Patrik Huber, Zhen-Hua Feng, William Christmas, Josef Kittler, Matthias Ratsch
Institutions: University of Surrey, Jiangnan University, Reutlingen University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1990655008, G7030354634, G7219166536
OpenAlex ID: W1487748529