Combination of facial landmarks for robust eye localization using the discriminative generalized Hough transform
ISSN der Zeitschrift
Regular Research Papers
Gesellschaft für Informatik e.V.
The Discriminative Generalized Hough Transform (DGHT) is a general and robust automated object localization method, which has been shown to achieve state-of-the-art success rates in different application areas like medical image analysis and person localization. In this contribution the framework is enhanced by a novel facial landmark combination technique which is theoretically introduced and evaluated for an eye localization task on a public database. The technique applies individually trained DGHT models for the localization of different facial landmarks, combines the obtained Hough spaces into a 3D feature matrix and applies a specifically trained higher-level DGHT model for the final localization based on the given features. In addition to that, the framework is further improved by a task-specific multi-level approach which adjusts the zooming-in strategy with respect to relevant structures and confusable objects. With the new system it was possible to increase the iris localization rate from 96.6% to 97.9% on 3830 evaluation images. This result is promising, since the variation of the head pose in the database is quite large and the applied error measure considers the worst of a left and right eye localization attempt.