Biometric authentication system based on texture features of retinal images

In biometric authentication system, distinct set of characteristic features are used to identify an authorised person. Retina is a stable biometric feature because of its location and unique physiological characteristics. In this paper, we propose a texture feature-based retinal authentication system. Texture features are considered as important features for authentication purpose. These texture features of retina are extracted using local configuration pattern (LCP) and Radon transform technique. The LCP computes the local structural information as well as the microscopic information of the image. Using Radon transform on retinal images, Radon features are extracted which contains the texture information of the blood vessels. A feature vector is formed by combining all theses LCP and Radon features and then fed to a feed-forward artificial neural network (FANN) classifier. This stage checks whether the test image belongs to the authorised person or not. Three general retinal databases DRIVE, HRF, Messidor, and images collected from two local eye hospitals are considered to authenticate a person. Two retinal authentication databases RIDB and VARIA are also used for evaluating the performance of the system. The results obtained show that the system is effective and efficient in authenticating the individuals.