Retinal biometric identification using convolutional neural network

Abstract

Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

“…Research [7] was using hit or miss transformation to obtain bifurcation and crossover points on retinal blood vessel images. Retinal biometric system feature extraction research was carried out [8] using preprocessing, where the output of this feature extraction process is a binary image of retinal blood vessel segmentation that has undergone various processes of eliminating nonvascular objects and improving image quality. The purpose of performing this image transformation is to increase the quantity of the existing image but still not eliminate the biometric features of the image.…”

“…There was an error in the extraction carried out in this study where the central branch artery and vein were detected as non-blood vessels so that this object was removed and considered as noise. The feature extraction results are entered into the CNN model in forming the retinal biometric system [8]. Research to detect bifurcation and crossover was done by using the morphological operations of opening, closing, and watershed transformation.…”