.png){kind=icon}
.png){kind=icon}
Return
Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization
.png){kind=icon}
OA
AI
DOI：10.1109/TSMC.2016.2562500.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional iris segmentation methods give good results when the iris images are taken under ideal imaging conditions. However, the segmentation accuracy of an iris recognition system significantly influences its performance especially in nonideal iris images. This paper proposes a novel segmentation method for nonideal iris images. Two algorithms are proposed for pupil segmentation in iris images which are captured under visible and near infrared light. Then, a fusion of an expanding and a shrinking active contour is developed for iris segmentation by integrating a new pressure force to the active contour model. Thereafter, a noncircular iris normalization scheme is adopted to effectively unwrap the segmented iris. In addition, a novel method for closed eye detection is proposed. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids of the iris images. Experimental results on CASIA V4.0, MMU2, UBIRIS V1, and UBIRIS V2 iris databases indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris segmentation algorithms revealed considerable improvement in segmentation accuracy and recognition performance while being computationally more efficient.
Keywords:
Active contour
biometrics
image segmentation
iris recognition
morphological operations
skin detection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W