Recent trends of ROI segmentation in iris biometrics: a survey

Segmentation in iris biometrics deals with the localisation of inner and outer boundaries of the iris and isolation of the region of interest (ROI) from the input eye image. The isolated ROI is further used to extract the meaningful features of iris for its effective representation. That is why accuracy of the segmentation module directly affects the overall accuracy in an iris recognition system. In view of this, the present study provides a comprehensive review of state-of-the-art methods on iris segmentation that were reported after 2011. Iris segmentation approaches based on eye images captured in both visible and near infrared illumination have been reviewed in this paper. The state-of-the-art iris segmentation approaches have been categorised into four broad classes, namely: integro-differential operator (IDO)-based approaches, circular Hough transform (CHT)-based approaches, deep learning-based approaches, and miscellaneous approaches. The sole purpose of this survey is to deliver insights on ROI segmentation, which is a prominent step of iris recognition process, and to suggest prospective research directions to the readers.