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.
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Kirchberg, K.J., Jesorsky, O., Frischholz, R.W. (2002). Genetic Model Optimization for Hausdorff Distance-Based Face Localization. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds) Biometric Authentication. BioAW 2002. Lecture Notes in Computer Science, vol 2359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47917-1_11
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DOI: https://doi.org/10.1007/3-540-47917-1_11
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