Recently, discriminative methods such as SVM has been widely used in object location. But there has been no method to perform well enough both at accuracy and speed. For linear SVM, it is hard to separate the nonlinear samples exactly. For kernel SVM, it is hard to be applied to real-time application, because of the computational cost kernel function. Local linear SVM has been proved to be a good tradeoff between fast linear SVM and qualitative best kernel methods. However, it is still time-consuming for real-time application. To design a high efficiency and high precision eye locahzer, first, we deduce a fast variation for LL-S VM which can serve as a more fast and accurate substitute of the traditional nonlinear kernel SVM. Second, to further improve the speed, we also adopt a candidate selection strategy. Extensive experiments on the BioID, FERET, FRGC, and LFW database show that our proposed method achieves favorable localization accuracy against other state-of-the-art methods at a speed as fast as 5ms to localize two eyes.