This paper introduces alternative solutions to recognize the state of eyes (being open or closed) and detect eye corners. First, the determination of the state is based on a binary dual-state classifier which is trained by boosting the proposed Local Intensity Increasing Patterns (LIIP) based feature vectors extracted from eye samples. LIIP encodes the increasing trend of local intensity pattern; Second, a novel correlation filter, the Synthetic Least Squares Filter (SLSF), is developed to detect eye corners. SLSF reversely constructs the filter to achieve a least squared error between the actual and synthetic correlation responses. Experimental results show the proposed methods can achieve favorable performance.
