This paper presents a novel retina biometric scheme that performs person verification based on passing 2 stages: robust feature points matching and edge dissimilarity measure. Our approach differs from those in the literature as we propose the use of edges and edge dissimilarity measure for retina verification. Our first-stage matching/authentication utilizes robust feature points' matching to determine tentatively whether there is a 'match' and if so, performs image registration between the test and template retina image. The robust feature points' matching is achieved in 2 steps: graph-based feature points' matching followed by pruning of wrongly matched feature points using a Least-Median-Squares estimator that enforces an affine transformation geometric constraint. To compute edge dissimilarity measure in our second-stage matching/authentication, we propose the 'robustified Hausdorff distance'. We show that our proposed approach outperforms two of the state-of-the-art approaches when tested on the same dataset.