Supervised retinal biometrics in different lighting conditions

Retinal image has been considered for number of health and biometrics applications. However, the reliability of these has not been investigated thoroughly. The variation observed in retina scans taken at different times is attributable to differences in illumination and positioning of the camera. It causes some missing bifurcations and crossovers from the retinal vessels. Exhaustive selection of optimal parameters is needed to construct the best similarity metrics equation to overcome the incomplete landmarks. In this paper, we extracted multiple features from the retina scans and employs supervised classification to overcome the shortcomings of the current techniques. The experimental results of 60 retina scans with different lightning conditions demonstrate the efficacy of this technique. The results were compared with the existing methods.