Reflection removal and feature extraction techniques in non-cooperative visible eye images for iris recognition

Abstract

Research on iris recognition system nowadays focuses on identifying a person in a non-cooperative environment by capturing an eye image in motion and at different distances. A visible wavelength illumination is used to capture the eye image which is believed to be safer to the eyes as excessive level of near infrared wavelength illumination can endanger the eye. However, the quality of data captured is very low and there are large reflections with different intensities in the eye image. These have caused incorrect segmentation of iris boundaries as well as the inability to extract texture features of an iris in a non-cooperative environment leading to a reduction in the iris recognition performance. The research proposed the development of two combined methods to improve the iris recognition system. The first combined method consists of line intensity profile and support vector machine and the second is a combination of multiscale sparse representation of local Radon transform. The former identifies and classifies between reflections and nonreflections whereas the latter performs three processes: reduces noise during down sample of normalized iris, extracts an iris texture in the different angles of orientation information and uses score combination of multiscale at the end of the process to increase the matching score. These two methods were tested against UBIRIS.v2 iris database and the results of iris recognition compared to the existing methods achieved an accuracy of more than 90%.