A Precise Eye Localization via a Dynamic Probability Distribution HMM Model
采用动态HMM概率分布模型的人眼精确定位

Accurately locating the human eyes in a single and gray level image with cluttered background remain a difficult problem in recent year.In this paper,we use a discrete Hidden Markov Model(HMM) that included explicit state duration density to locate the human eye precisely.At first,a retinal sampling grid and a special method are adopted to extract the observation sequence.We do not need rotation and scaling transformation and matching operation,for we use a dynamic observation symbol probability distribution in state to adapt the various angles of human eyes in image,and a dynamic sampling model that controls the sizes of the retinal sampling grid through the evaluation of the observation sequence to adapt to the various sizes of human eyes in image.The experiment results show that our algorithm is effective and robust and have high positioning accuracy.