Precise eye center localization in a practical environment

Eye center localization plays a crucial role in computer vision and pattern recognition applications such as face recognition, gaze estimation, driver drowsiness detection, etc. Accurate eye center localization is challenging because of face pose orientation, illumination variations, eye occlusion, and specular reflections. This paper proposes a precise eye center localization method to solve the problems stated above. In this paper, multi-iris shape features extract the number of possible eye locations in the upper half of the face region, reducing the non-eye regions and improving computational speed. Then, Convolutional neural networks (CNN) extract high-level features, and the support vector machine (SVM) classifier is used for classification. Experiments were performed on AR, CASPAEL, and BioID databases to test the performance of the proposed model under varying pose, occlusion, and illumination variations, etc. Extensive experiments suggest that the proposed method provides better performance as compared to other existing methods.