A hybrid classifier for precise and robust eye detection

Eye location is an important visual cue for face image processing such as alignment before face recognition, gaze tracking, expression analysis, etc. In this paper a novel eye detection algorithm is presented, which integrates the characteristics of single eye and eye-pair images to develop a hybrid classifier under the learning paradigm. The low dimensional features representing eye patterns yield by subspace projection are selected via a filter and a wrapper method for a simplified maximum likelihood and a SVM classifier respectively. Eye candidates determined by a cascade of the two classifiers are further verified with eye-pair template matching scores to reject false detections. The performance of this eye detector is assessed on several publicly available face databases and the experimental results demonstrate its robustness to the variations in head pose, facial expressions, partial occlusions and lighting conditions