Visible iris imaging: A novel imaging solution for improved iris recognition

Abstract

Imaging dark iris in visible spectrum has had limited success due to low texture visibility owing to light-scattering and absorption properties of the cells in iris. Traditional iris imaging employ Near-infra-red (NIR) illumination to address such a problem, however, this limits the use of biometrics using regular cameras such as the ones present on smartphones. In this work, we propose a new iris imaging framework, to resolve the iris texture pattern without employing the NIR illumination. The proposed iris imaging setup employs a simple illumination source placed at an acute angle to axis of eye and imaging device to maximize the texture visibility. The proposed setup is used to obtain a new iris image database to evaluate the verification performance. The newly constructed iris image database of dark iris images comprises of 62 unique iris patterns with 10 samples each in different session. The database is acquired using iPhone 5S smartphone. Further a benchmark comparison is provided with respect to NIR images for the subset of the database to measure the robustness of proposed method. Detailed experiments are carried out using five well established state-of-art iris recognition algorithms and have indicated the superior performance of the proposed imaging setup with Genuine Match Rate (GMR) of 85.98% at False Match Rate (FMR) = 0.01% with an Equal Error Rate (EER) of 3.54%.