Biometric identification, or biometrics, refers to identifying an individual based on his or her distinguishing characteristics. More precisely, biometrics is the science of identifying, or verifying the identity of, a person based on physiological or behavioral characteristics. Retinal Recognition (RR) seeks to identify a person by comparing images of the blood vessels in the back of the eye, the retinal vasculature. This method takes advantage of the fact that of all human physiological features, the retinal image is the best identifying characteristic. Because of the complex structure of the salient features of the retinal vessels, each person's retina and also each person's eye is unique. Retinal vessel landmarks are: bifurcation and end points. Due to its unique and unchanging nature, the retina appears to be the most precise and reliable biometric. This article describes an algorithm for automatic vessel tree segmentation and vascular landmarks extraction from retinal fundus images. The propose method is composing of 3 main processing stages: a preprocessing step, a main process step, and a post processing step. The preprocessing step consists of 3 stages): a) Green-color band selection, b) Mask generation, c) Image enhancement for vessel network detection. The main process consists of 4 stages: a) Cooccurrence matrix calculation, b) Vessel segmentation by the Second Entropy thresholding, c) Morphological thinning, and d) Landmarks detection. And the post processing step contains 2 sub stages: e) Pruning, and f) Landmark attributes estimation. The “eye print” representation is constructed using this salient features. The obtained results shown the effectiveness and accuracy of the propose method to detect and extract information from a retinal fundus images. The elapsed time for the propose method is 8 seconds.