A biometric authentication system provides an automatic person authentication based on some characteristic features possessed by the individual. Among all other biometrics, human retina is a secure and reliable source of person recognition as it is unique, universal, lies at the rare end of the eye and hence it is unforgeable. The process of authentication mainly includes pre-processing, feature extraction and then features matching and classification. Also authentication systems are mainly appointed in verification and identification mode according to the specific application. In this paper, pre-processing and image enhancement stages involve several steps to highlight interesting features in retinal images. The feature extraction stage is accomplished using a bank of Gabor filter with number of orientations and scales. Generalized Discriminant Analysis (GDA) technique has been used to reduce the size of feature vectors and enhance the performance of proposed algorithm. Finally, classification is accomplished using k-nearest neighbor (KNN) classifier to determine the identity of the genuine user or reject the forged one as the proposed operate in identification mode.
In biometrics field, usually feature vectors have major length and contain ineffective information. This problem is so called "curse of dimensionality". Hence, there is a need for efficient dimensionality reduction technique to remove the redundant features and reduce the size of feature vectors to get high accuracy rate with fast performance. In this paper a comprehensive study of commonly used dimensionality reduction techniques: Principle Component Analysis, Linear Discremenant Analysis, and Generalized Discremenant Analysis, have been handled. Theoretical background of these techniques is illustrated along with the methods used to calculate their projection spaces then; practical implementation is conducted to find out and adopt the best one for retina based biometric authentication system. From this extensive study, it has been concluded that PCA technique has a number of problems make it has a bad classification power. LDA technique has a number of problems make it impossible to implement in most cases of biometrics field, while GDA technique is more efficient than the PCA and LDA techniques for dimensionality reduction purpose. It has high classification power and consumes less computational time. Hence, GDA technique is adopted in the proposed authentication system.
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