A Study of Image Enhancement for Iris Recognition

Abstract

Iris recognition is one of the most prevalent methods for personal identification in the modern day. Low quality iris images that are blurry, of low resolution, and poor illumination poses a big challenge for iris recognition as the iris recognition efficiency is entirely dependent on whether the image supplied is of good quality. Therefore, several enhancement techniques have been proposed and used for image processing to increase the quality of iris images. Although there is no best approach for all types of image enhancement, histogram equalization (HE) is a commonly used approach as it is a simple yet effective method. In this paper, several enhancement techniques based on histogram equalization approaches, such as histogram equalization (HE), brightness preserving bihistogram equalization (BBHE), dynamic histogram equalization (DHE), adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), dualistic sub-image histogram equalization (DSIHE), and multi-scale adaptive histogram equalization (MAHE), have been explored. A comparison is conducted based on the image quality of all image enhancement techniques proposed in this paper. 

Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images

“…Basically, the philosophy of the contrast enhancement process i.e., to enhance the quality of image by enlarging the gray level image. The common of contrast enhancement process used is based on histogram equalization (HE) method [8]- [11] due to the simplicity and highly effectiveness. However, the major drawback of this method is losing the originality of image, loss of image information, over enhancement of brightness as well as the contrast and amplified the noise from the original image.…”

“…Thus, this study proposed pre-enhancement process with the two well know contrast enhancement methods which is HE and CLAHE. Basically, the HE method will enhance the entire contrast image by enhancing the whole image histogram into the uniform histogram [8]. Meanwhile the CLAHE method modifies the contrast by each of small windows value based on some characteristics and it is applied to improve the contrast and minimize the noise that presence in the image [23].…”