Efficient and Fast Iris Localization Using Binary Radial Gradient Features for Human–Computer Interaction
Hong‐In Kim, Jinbum Kim, Rae‐Hong Park · International Journal of Pattern Recognition and Artificial Intelligence · 2017
This paper proposes an efficient and fast iris localization method. It uses support vector machine learning of iris features that represent closed outer and inner iris boundaries encompassing a low-intensity region. In addition, depending on the location of the iris in an eye image, an iris detection method is proposed based on three sub-datasets of eye images (middle, right, and left sub-datasets) with different iris features. The proposed method is implemented using fast sliding window and fast computation of the iris detection score with binary features. Compared with state-of-the-art methods, experimental results show that the proposed method is twice as fast and has comparable accuracy, even when factoring in head rotation, glasses, and highlights.