Optimal Parameterization of Point Distribution Models Optimal Parameterization of Point Distribution Models Acknowledgements: This Work Has Been Performed with Nancial Support from the Swiss National

We address the problem of determining the optimal model complexity for shape modeling. This complexity is a compromise between model speciicity and generality. We show that the error of a model can be split into two components, the model error and the tting error, of which the rst one can be used to optimize the model complexity based on the speciic application. This strategy improves over traditional approaches, where the model complexity is only determined by vague heuristics or trial-and-error. A method for the determination of optimal active shape models is proposed and its eeciency is validated in several experiments. Furthermore, this method gives an indication on the range of valid shape parameters and on whether or not an increased number of training data will reduce the number of shape parameters further.