Personal verification system based on retina and SURF descriptors

Today, Human recognition, especially based on retina, has been an important and attractive topic of scientific research. Most efforts in Biometrics tend to develop more efficient systems which compromise speed and robustness of authentication. In fact, retinal images often suffer from imperfections such as background intensity variation, affine transformations (translation, rotation, scale changes, etc.) variations from pattern to other. These defects can seriously affect features extraction in terms of quality and execution time. In this context, in order to overcome these defects, we propose in this paper a novel retinal verification system based on the Speeded Up Robust Features (SURF) extraction. This feature extraction method is so fast and invariant to the affine transformations such as rotation, scale changes and translation. We employ the Optical Disc interest Ring (ODR) method as a preprocessing step in order to further speed up the system and improve the performance. A subset of the VARIA database is used to evaluate the proposed SURF based system. It compromises a high quality with 100% of verification accuracy rate and a time processing very lower than existing verification systems.