Eye Localization Using the Discriminative Generalized Hough Transform

Abstract

The Discriminative Generalized Hough Transform (DGHT) has been successfully introduced as a general method for the localization of arbitrary objects with well-defined shape in medical images. In this contribution, the framework is, for the first time, applied to the localization of eyes in a public face database. Based on a set of training images with annotated target points, the training procedure combines the Hough space votes of individual shape model points into a probability distribution of the maximum-entropy family and optimizes the free parameters of this distribution with respect to the training error rate. This assigns individual positive and negative weights to the shape model points, reflecting important structures of the target object and confusable shapes, respectively. Additionally, the estimated weights allow to determine irrelevant parts in order to eliminate them from the model, making space for the incorporation of new model point candidates. These candidates are in turn identified from training images with remaining high localization error. The whole procedure of weight estimation, point elimination, testing on training images and incorporation of new model point hypotheses is iterated several times until a stopping criterion is met. The method is further enhanced by applying a multi-level approach, in which the searched region is reduced in 6 zooming steps, using individually trained shape models on each level. An evaluation on the PUT face database has shown that the system achieves a state-of-the-art success rate of 99% for iris detection in frontal-view images and 95% if the test set contains the full head pose variability.