Genetic Model Optimization for Hausdorff Distance-Based Face Localization
Abstract
In our previous work we presented a model-based approach to perform robust, high-speed face localization based on the Hausdorff distance. A crucial step during the design of the system is the choice of an appropriate edge model that fits for a wide range of different human faces. In this paper we present an optimization approach that creates and successively improves such a model by means of genetic algorithms. To speed up the process and to prevent early saturation we use a special bootstrapping method on the sample set. Several initialization functions are tested and compared.
Cite
Text
Kirchberg et al. "Genetic Model Optimization for Hausdorff Distance-Based Face Localization." European Conference on Computer Vision, 2002. doi:10.1007/3-540-47917-1_11Markdown
[Kirchberg et al. "Genetic Model Optimization for Hausdorff Distance-Based Face Localization." European Conference on Computer Vision, 2002.](https://mlanthology.org/eccv/2002/kirchberg2002eccv-genetic/) doi:10.1007/3-540-47917-1_11BibTeX
@inproceedings{kirchberg2002eccv-genetic,
title = {{Genetic Model Optimization for Hausdorff Distance-Based Face Localization}},
author = {Kirchberg, Klaus J. and Jesorsky, Oliver and Frischholz, Robert},
booktitle = {European Conference on Computer Vision},
year = {2002},
pages = {103-111},
doi = {10.1007/3-540-47917-1_11},
url = {https://mlanthology.org/eccv/2002/kirchberg2002eccv-genetic/}
}