Eye corner detection is important for eye extraction, face normalization, other facial landmark extraction and so on. We
present a feature-based method for eye corner detection from static images in this paper. This method is capable of
locating eye corners automatically. The process of eye corner detection is divided into two stages: classifier training and
classifier application. For training, two classifiers trained by AdaBoost with Haar-like features, are skillfully designed to
detect inner eye corners and outer eye corners. Then, two classifiers are applied to input images to search targets. Eye
corners are finally located according to two eye models from targets. Experimental results tested on BioID face database
and our own database demonstrate that our method obtains a high accuracy under clutter conditions.