Facial feature extraction in people's frontal view images

We introduce a novel method for facial feature extraction. In our approach, we attempt to find the correspondence of an intensity grid, where a feature template is defined, to an image patch. Their similarity is measured with the Sampling Determination Coefficient, the square of the Linear Correlation Coefficient. The search space generated by this function make it easier for an optimization algorithm to find the parameters to extract the sought feature. We extract facial features from people's frontal view images, like the ones present in most photographs of passports, driver licenses and other documents alike. We tested our algorithm with 823 images. Facial features were correctly extracted in 99.028% of them.