Eyelid and eyelash detection method in the normalized iris image using the parabolic Hough model and Otsu’s thresholding method

Eyelids and eyelashes occluding the iris region are noise factors that degrade the performance of iris recognition. If they are incorrectly classified as an iris region, the false iris region information decreases the recognition rate. Thus, reliable detection of eyelids and eyelashes is required to improve the performance of iris recognition. In this paper, we propose an automatic eyelid and eyelash detection method based on the parabolic Hough model and Otsu’s thresholding method. By applying the parabolic Hough transform to the normalized iris image, rather than to the original image, we reduce the dimension of the parameter space and limit the parameter search range, decreasing the computational load. In addition, for automatically separating the eyelash region we apply Otsu’s method to the proposed feature that is obtained by combining the intensity and local standard deviation values. The proposed method is applied to the CASIA version 3 database and the performance of the proposed and six existing methods is assessed in terms of the decidability, equal error rate, and detection error trade-off curve. In terms of these performance measures, the proposed method shows the better performance than conventional methods.