Complex Bingham Distribution for Facial Feature Detection

We present a novel method for facial feature point detection on images captured from severe uncontrolled environments based on a combination of regularized boosted classifiers and mixture of complex Bingham distributions. The complex Bingham distribution is a rotation-invariant shape representation that can handle pose, in-plane rotation and occlusion better than existing models. Additionally, we regularized a boosted classifier with a variance normalization factor to reduce false positives. Using the proposed two models, we formulate our facial features detection approach in a Bayesian framework of a maximum a-posteriori estimation. This approach allows for the inclusion of the uncertainty of the regularized boosted classifier and complex Bingham distribution. The proposed detector is tested on different datasets and results show comparable performance to the state-of-the-art with the BioID database and outperform them in uncontrolled datasets.