Efficient and Fast Iris Localization Using Binary Radial Gradient Features for Human–Computer Interaction

Abstract

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.