Iris Segmentation & Recognition in Unconstrained Environment (International Journal of Engineering and Computer Science, 2014)
Recently, Iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. In this paper, a new algorithm is being proposed to segment the iris images captured in visible wavelength under unconstrained environment. The proposed algorithm uses the various types of filters to smooth the iris image. The proposed algorithm reduces the error percentage even in the presence of noise include iris obstruction and specular reflection. The proposed algorithm starts with the acquired iris image and determining the expected region of the iris using the Fuzzy C-means clustering technique. The Canny Edge detection is used to detect the edges of the iris or eye. The Hough Transform is employed to estimate the iris radius and center. After detecting the edges and Hough Transform, a new efficient algorithm is developed to detect the upper or lower eyelid. Finally, the non-iris regions are removed and the results of the proposed algorithm on our iris image database demonstrate that it improves the segmentation accuracy and time.