A robust iris localization scheme for the iris recognition

Iris Localization Algorithm based on Effective Area

“…In order to verify the effectiveness of the improved algorithm, comparative experiments were carried out to determine whether the use of YOLOv4 to extract the effective iris area aids the proposed iris localization method, the effect of reflective noise removal, and that of outer boundary adjustment. At the same time, in order to verify the performance of the proposed algorithm, we compared with other excellent iris localization methods [8,10,13]. e algorithm parameters were set as follows: e area where the center of the inner boundary may appear was set as a square with a side length of p � 100, the maximum radius of the inner boundary r p2 was set to one-third of the long side of the effective iris area, the side length q of the reflective noise removing range was the sum of the inner boundary center range and the inner boundary diameter, and the center range of the outer boundary was k � 20. e sets of the IDO parameters reserved was z � 3, the extend distance of radius Δn � 2.…”

“…where G σ (r) is the Gaussian smoothing function with a scale of σ; * represents convolution; r, x 0 , y 0 are the integration radius and center, respectively; I (x, y) is the original image of iris. Existing methods generally use bilinear interpolation [13] or morphological methods [9] to avoid the interference of reflective noise. However, the essence of bilinear interpolation is to select the average grayscale value of four pixels in the vertical and horizontal directions according to a fixed distance to replace the current pixel, so it is susceptible to the interpolation distance when performing reflective noise filtering.…”

“…In order to verify the performance of the proposed algorithm, we compared it with the algorithms presented in the literature [8,10,13]. All algorithms completed localization based on the effective iris area extracted by YOLOv4.…”

“…But when the difference in grayscale values of pupil and iris, and background is not obvious, the localization effect of this method is limited. Jan et al [13] used the bilinear interpolation scheme to remove the reflective noise in image and set the threshold according to continuous low grayscale pixels to binarize the image. en, the inner boundary of the iris was roughly identified using the largest connected area directly, and the outer boundary was roughly determined using the CHT.…”

Assessing Porcine Iris Elasticity and Mechanical Anisotropy with Optical Coherence Elastography

“…The exterior surface is divided into two different portions by the collarette. The collarette is the zone where the sphincter and the dilator muscles overlap [1][2][3]. The iris possesses two layers, the stroma, which contains most of the vascular tissue and is connected to the sphincter and dilator muscles, and the posterior, which is the pigmented layer mainly composed of melanocytes and fibroblasts [4,5].…”