Abstract: We describe a novel eye detection method that is robust to the obstacles such as surrounding illumination, hair, and eye glasses. The obstacles above a face image are constraints to detect eye position. These constraints affect the performance of the face applications such as face recognition, gaze tracking, and video indexing systems. To overcome this problem, the proposed method for eye detection consists of three steps. First, the self quotient images are applied to the face images by rectifying illumination. Then, unnecessary pixels for eye detection are removed by using the symmetry object filter. Next, the eye candidates are extracted by using the gradient descent which is a simple and a fast computing method. Finally, the classifier, which has trained by using AdaBoost algorithm, selects the eyes from all of the eye candidates. The usefulness of the proposed method has been demonstrated in an embedded system with the eye detection performance.