Real time iris segmentation quality evaluation using medoids

Ejiogu, Ugochi U.C.; Iloanusi, Ogechukwu N.

doi:10.1016/j.patcog.2022.109290

Cited by 3 publications

(1 citation statement)

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“…They used high‐resolution eye images. Ejiogu and Iloanusi (2023) proposed iris segmentation and localization using medoids in high‐resolution eye images. Their performance degrades in low‐resolution images captured in a non‐cooperative environment.…”

Section: Related Workmentioning

confidence: 99%

A cascaded deep learning framework for iris centre localization in facial image

Ahmad,

Muhammad,

Yadav

et al. 2023

Expert Systems

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Accurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure SED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS‐PEAL for pose variations.

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Section: Related Workmentioning

confidence: 99%