A novel eyelid detection method for iris segmentation

Abstract

The proper segmentation of the iris image determines the iris recognition accurate to a great extent. Most of the iris images are covered by upper or lower eyelids, thus it is essential to detect the eyelid boundary for improving the iris recognition accuracy furthermore. An eyelid detection method based on maximal connection path is presented in this paper. After the preprocessing of the iris image, the horizontal segmentation operator and image binarization are used to extract the eyelid edge information. The eyelids span the whole image in horizontal direction and the average of vertical gradients is larger in the area with eyelid boundary, therefore, the horizontal distance of the connection area with eyelid boundary should be the longest one in the edge image. In use of this feature, the candidate edge points of eyelid boundary are detected. Eventually, the eyelid boundaries are modeled with the parabola curves. The algorithm performance is tested in CASIA Iris Database, and experiment results show that about 0.117 second at speed and 88.9% at precision are reached for the upper eyelid detection, and about 0.078 second at speed and 98.5% at precision for the lower eyelid detection. In comparison with Daugman's method, this algorithm enhances the detection speed largely and shows good accuracy.