Improving the Performance of Iris Recogniton System Using Eyelids and Eyelashes Detection and Iris Image Enhancement

Xu, Guangyan; Zhang, Zaifeng; Ma, Yide

doi:10.1109/coginf.2006.365606

Cited by 30 publications

(17 citation statements)

References 5 publications

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“…Masek [8] has proposed a method for the eyelid segmentation in which the iris and the eyelids have been separated through Hough transformation. In [14], Xu et al have segmented out the upper and lower eyelid candidate region into 8 sub-blocks. Out of these 8 sub-blocks, the proper eyelid-eyelash model has been chosen based on maximum deviation from each block.…”

Section: Related Workmentioning

confidence: 99%

Fast Approximate Eyelid Detection for Periocular Localization

Hazarika

Prakash

Bakshi

et al. 2014

Smart Innovation, Systems and Technologies

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Abstract.Iris is considered to be one of the most reliable traits and is widely used in the present state-of-the-art biometric systems. However, iris recognition fails for unconstrained image acquisition. More precisely, the system cannot properly localize the iris from low quality noisy unconstrained image, and hence, the successive modules of biometric system fails. To achieve recognition from unconstrained iris images, the periocular region is considered. The periocular (periphery of ocular) region is proven to be a trait in itself and can serve as a biometric to recognize human, though with a lower accuracy compared to iris. In this paper, we propose a novel technique to localize periocular region on the basis of eyelid information extracted from eye image. The proposed method will perform periocular localization successfully even when iris detection fails. Our method detects the horizontal edges as eyelids and the rough map of eyelids gives the radius of iris, which is used to anthropometrically derive the periocular region. The proposed method has been validated on standard publicly available databases : UBIRISv1 and UBIRISv2, and is found to be satisfactory.

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Section: Related Workmentioning

confidence: 99%

Fast Approximate Eyelid Detection for Periocular Localization

Hazarika

Prakash

Bakshi

et al. 2014

Smart Innovation, Systems and Technologies

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“…Besides, iris also has the uniqueness, stability and non-invasive characteristics [1,2]. Compared with other biometrical identification systems, iris identification has higher accuracy [3][4][5][6][7][8]. The performance of the iris identification system much depends on the accuracy of the iris location.…”

Section: Introductionmentioning

confidence: 97%

“…Nowadays, the main eyelids and eyelashes detection methods include: Xu et al and Daugman [3,4] proposed the circular arc method to detect fluctuation eyelids and statistical method to eliminate eyelashes. He et al and Tan et al [14,15] put up a curvature model and an automation parabolic method to detect eyelids, and proposed a prediction model and the gray threshold value method to deal with eyelashes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Iris Segmentation Method Based on Multi-Line

Jia

et al. 2013

Proceedings of the Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

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In the iris authentication system, the existence of eyelids and eyelashes noises might generate negative effect on pattern analysis. This paper proposes a novel and accurate iris segmentation method which adequately considers the edges and statistical feature to detect the eyelids and eyelashes noises in the captured iris images. First, multi-scale edge detection is used to get the iris coarse locations, and rank filter is employed to smooth images for determining a more accurate searching area of eyelids. Second, morphological operations as well as line Hough transform are presented to reserve the available edge points for multi-line eyelids fitting. Specially, the adaptive average intensity of individual iris image based on region of interest (ROI) is educed to get the statistical threshold for eyelashes detection. Experimental results indicate that the proposed method can effectively remove the occlusion caused by eyelids and eyelashes, and increase the amount of information (AOI) of segmented iris and improve the iris location accuracy.

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“…the iris boundaries using a best-fit approach with a priori models [13], [14], or they locate the iris by analyzing local image features [15], [16]. Other iris segmentation methods follow hybrid/incremental approaches that initially estimate the position of the iris, and then they refine the localization/segmentation [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Agent-based image iris segmentation and multiple views boundary refining

Labati

Piuri

Scotti

2009

2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems

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Abstract-The paper presents two different methods to deal with the problem of iris segmentation: an agent-based method capable to localize the center of the pupil and a method to process the iris boundaries by a multiple views approach. In the first method, an agent corresponds to the coordinates of a specific point of analysis in the input image. A population of agents is deployed in the input image, then, each agent collects local information concerning the intensity patterns visible in its region of interest. By iterations, an agent changes its position accordingly to the local properties, moving towards the estimation of the pupil center. If no available information is present in its region of interest, the agent will move itself along a random walk. After few iterations, the population tends to spread and then concentrate in the inner portion of the pupil. Once the center of the pupil has been located, the inner and outer iris boundaries are refined by an approach based on multiple views analysis. This method starts with a set of points that can be considered as an approximation of the pupil center. For each point, a detailed estimation of the iris boundaries is computed, and the final description of the iris boundaries is obtained by merging all the obtained descriptions. The two methods were tested using CASIA v.3 and UBIRIS v.2 images. Experiments show that the proposed approaches are feasible, also in eye images taken in noisy or non-ideal conditions, achieving a total error segmentation accuracy up to 97%.

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Improving the Performance of Iris Recogniton System Using Eyelids and Eyelashes Detection and Iris Image Enhancement

Cited by 30 publications

References 5 publications

Fast Approximate Eyelid Detection for Periocular Localization

Fast Approximate Eyelid Detection for Periocular Localization

A Novel Iris Segmentation Method Based on Multi-Line

Agent-based image iris segmentation and multiple views boundary refining

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