Iris Localization Algorithm based on Effective Area

Abstract

Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

Robust iris localization algorithm based on improved YOLOv4 and self-organized particle swarm optimization

“…In recent years, researchers have applied deep learning methods to iris localization. Yu [9] used YOLOv4 to extract and cut the rough location center of the entire iris, and improved the radial difference method to adjust the inner and outer circle boundaries to complete the localization. Xiong [10] used the YOLOv4 model to approximate the pupil center, and the improved integral-differential operator accurately located the iris boundary.…”

Retracted: Iris Localization Algorithm based on Effective Area

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”