Retinal vascular network pattern is unique to each individual which can be used for person identification in biometric authentication. In this study, the authors have proposed a novel biometric authentication method using retinal vascular branch, bifurcation and crossover points (i.e. feature points). The method automatically extracts the vascular network from colour retinal images and identifies these feature points. The major blood vessels characterised by width and length are identified from the segmented vascular network. For this, a novel vessel width measurement method is applied and vessels more than certain widths are selected as major vessels following an established protocol. The geometric hashing technique is developed to compute the invariant features from these feature points. They consider the feature points from major vessels which will be less susceptible to noise for modelling a basis pair and all other points together for locations in the hash table entries. The models are invariant to rotation, translation and scaling as inherited from geometric hashing. For each person, the system is trained with the models to accept or reject a claimed identity. They have tested their method on 3010 retinal images and achieved 96.64% precision and 100% recall.