Noise reduction in iris recognition using multiple thresholding

Iris recognition is known to be as one of the most accurate biometric modalities. In iris image processing, the unwanted textures in the iris region such as those belong to pupil, eyelashes, eyelids, shadows and light reflections are defined as noises. These unwanted noises have strong gray values which cause wrong threshold value selection and thus, result in reducing the performance of the iris recognition system. In this paper, we proposed a multiple thresholding method for detection of eyelids, eyelash textures and light reflections and pupil pixels. The threshold values related to these noises are selected based on the information obtained from the histogram of the normalized iris image. The proposed method was applied to the CASIA V.3 iris image database, version three, from the institute of automation, Chinese academy of science and has 99.62% recognition rate with 0.04 false rejection rate (FRR).