The proposed method utilized both vascular and non-vascular features for identification and yields recognition rates of 100 % and 92.5% respectively.īiometrics Fundus imaging Retinal identification Retinal imaging.
#Normal retina fundus photo verification#
These research papers proposed retinal recognition algorithms for biometric verification and identification. The dataset is supported by research work and. Wide field imaging (WFI) and ultra wide field imaging (UWFI) are now increasingly popular. The data presented in the paper is composed of 100 retinal images of 20 individuals (5 images were captured from each patient). Retinal imaging techniques have evolved at a remarkable pace in the last two decades. This method lies in the use of Morphological Component Analysis (MCA) algorithm to separate lesions from normal retinal structures to facilitate the detection. Moreover, the vascular pattern in the retina is unique and remains unchanged during the entire life span. Since the retina is embedded inside the eye thus is least affected by the outer environment and retain in its original state. Amongst all of them, retina based identification is considered as the spoof proof and most accurate identification system. Biometric verification includes behavioural (voice, signature, gait), morphological (Fingerprint, face, palm print, retina) and biological (Odour, saliva, DNA) features. However, the fear of decryption and hacking retained. Before biometrics, the information was secured through passwords, pin keys, etc. It’s widely available, easy to use, and is very good at documenting the appearance of the optic nerve and existence of blood buildup in the eye. Virtually every ophthalmologist in the country has a fundus camera, Dr. Biometric recognition has become an integral part of any organization's security department. Color fundus photography captures 30- to 50-degree views of the retina and optic nerve.
Retinal recognition is considered as one of the reliable biometric recognition features. The abovementioned dataset holds a significant position in retinal recognition and identification. The stated dataset contains Retinal fundus images acquired using Fundus imaging camera TOPCON-TRC 50 EX. The paper describes a dataset, entitled Retina Identification Database (RIDB).
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