The eye center localization is a crucial requirement for various human-computer interaction applications such as eye gaze estimation and eye tracking. However, although significant progress has been made in the field of eye center localization in recent years, it is still very challenging for tasks under the significant variability situations caused by different illumination, shape, color and viewing angles. In this paper, we propose a hybrid regression and isophote curvature for accurate eye center localization under low resolution. The proposed method first applies the regression method, which is called Supervised Descent Method (SDM), to obtain the rough location of eye region and eye centers. SDM is robust against the appearance variations in the eye region. To make the center points more accurate, isophote curvature method is employed on the obtained eye region to obtain several candidate points of eye center. Finally, the proposed method selects several estimated eye center locations from the isophote curvature method and SDM as our candidates and a SDM-based means of gradient method further refine the candidate points. Therefore, we combine regression and isophote curvature method to achieve robustness and accuracy. In the experiment, we have extensively evaluated the proposed method on the two public databases which are very challenging and realistic for eye center localization and compared our method with existing state-of-the-art methods. The results of the experiment confirm that the proposed method outperforms the state-of-the-art methods with a significant improvement in accuracy and robustness and has less computational complexity.