Fast normalized cross-correlation based retinal recognition

In this paper, a simple biometric scheme based on RGB retinal fundus images is proposed. First, prominent vasculature energy based feature vectors are constructed from RGB retinal fundus images to utilize the unique pattern of retinal vasculature. Next, fast normalized cross-correlation based feature matching is employed for person identification on publicly available DRIVE and STARE databases. This method excels recently published literatures with perfect recognition accuracy of 100% on STARE and accuracy of 99.77% on DRIVE databases. Its smaller dimension of feature vector and better recognition result make this method eligible for real-time person identification scheme.