Driver's drowsiness is one of the major causes of road accidents. The monitoring of a given driver's eye state by the use of a camera is considered to be a promising way to detect driver's drowsiness due to its accuracy and non-intrusiveness. However, eye location remains a challenging vision problem because of the constantly changing of illumination and driver's pose. Active shape model (ASM) is introduced in this paper to align the face. Though the ASM is a powerful statistical tool, it can suffer from changes in illumination and posture. Three contributions are involved in this paper. First, in order to maximize the tolerance of the ASM algorithm to illumination changes, we propose a robust ASM method with a novel local texture model learned from the self-quotient image instead of the original image. Second, a double layer overall shape model is proposed to enhance the adaptability of ASM. Third, strong constraints are achieved by an on-line learning of the distribution characteristics of the model parameters. The results show that the proposed algorithm is robust to the variation of illumination and driver's pose.