Facial feature extraction by a cascade of model-based algorithms

In this paper, we propose a cascaded facial feature-extraction framework employing a set of model-based algorithms. In this framework, the algorithms are arranged with increasing model flexibility and extraction accuracy, such that the cascaded algorithm can have an optimal performance in both robustness and extraction accuracy. Especially, we propose a set of guidelines to analyze and jointly optimize the performance relation between the constituting algorithms, such that the constructed cascade gives the best overall performance. Afterwards, we present an implementation of the cascaded framework employing three algorithms, namely, sparse-graph search, component-based texture fitting and component-based direct fitting. Special attention is paid on the search and optimization of the model parameters of each algorithm, such that the overall extraction performance is greatly improved with respect to both reliability and accuracy.