In this paper, we present an adaptive appearance based face recognition framework that combines the efficiency of global approaches and the robustness of local approaches together. The framework uses a novel eye locator to select an appropriate scheme for appearance based recognition. The eye locator first locates eye candidates via a new strength assignment, determined by the dissimilarity between the local appearance of an image point and the appearance of its neighboring points. Then the eye locator applies a simple but flexible model (half-circle snake) to the local context of the eye candidates in order to either refine the location of an eye candidate or discard non-eye candidates. We show the performance of our framework by testing on challenging face datasets containing extreme expressions, severe occlusions, and varied lighting conditions.