Fast and Accurate Pupil Isolation Based on Morphology and Active Contour

Abdullah, Mohammed A. M.; Dlay, S.S.; Woo, Wai Lok

doi:10.7763/ijiee.2014.v4.476

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…If the iris image is taken under NIR light, the pupil will appear darker than its surrounding with high contrast between the pupil and the iris region (see Fig. 2 (a)) [37]. Hence, methods based on thresholding are convenient for isolating the pupil from the rest of the iris image and finding its coarse parameters.…”

Section: A Pupil Segmentationmentioning

confidence: 99%

Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization

Abdullah

Dlay

Woo

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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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.

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Section: A Pupil Segmentationmentioning

confidence: 99%

Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization

Abdullah

Dlay

Woo

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…In our previous work on pupil segmentation [21], we used thresholding for reflection removal. However, to make this step more robust and adaptive, anisotropic diffusion has been used in this paper.…”

Section: A Inner Iris Boundary Detectionmentioning

confidence: 99%

Fast and accurate method for complete iris segmentation with active contour and morphology

Abdullah

Dlay

Woo

2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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the performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non-ideal iris images. This paper proposes a fast and accurate method for segmenting the iris using a combination of morphological operations and Chan-Vese active contour model. The morphological operations are used to determine a rough boundary of the iris region while the active contour is used to find the precise boundary. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids and the eyelashes of the iris images. Experimental results on the CASIA version 4.0 iris database have indicated a high level of accuracy using the proposed technique.

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“…For this step, PDIF applies a morphological closing with a 7x7 elliptical kernel. The choice for the kernel dimension has been based upon the values adopted in similar algorithms which performs morphological operations, such as the one proposed in [44], and the medium glint dimension computed on the collections adopted in the evaluation phase. These are reasonable assumptions since we are working on images provided by a head-mounted IR tracker, thus, the distance between the IR illuminator and the eye should be almost steady, as well as the glint dimension.…”

Section: B Novel Approachmentioning

confidence: 99%

PDIF: Pupil Detection After Isolation and Fitting

2020

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Pupil detection plays a key role in eye and gaze video-based tracking algorithms. Various algorithms have been proposed through the years in order to improve the performances or the robustness in real-world scenarios. However, the development of an algorithm which excels in both execution time and pupil detection precision is still an open challenge. This paper presents a novel, feature-based eyetracking algorithm for pupil detection. Morphological operators are used to remove corneal reflections and to reduce noise in the pupil area prior to the pupil detection step: this solution allows to significantly reduce the computational overhead without lowering the tracking precision. Moreover, a shape validation step is performed after the elliptical fitting and, if the elliptical shape is not detected properly, a set of additional steps is performed to improve the pupil estimation. The proposed solution, Pupil Detection after Isolation and Fitting (PDIF), has been compared with other state-of-the-art tracking algorithms that use morphological operations such as ElSe (Ellipse Selection) and ExCuSe (Exclusive Curve Selector) to evaluate both speed and robustness; the proposed algorithm has been tested over numerous datasets offering different pupil detection challenges. Obtained results show how PDIF provides comparable tracking precision at a significantly lower computational cost compared to ElSe and ExCuSe. INDEX TERMS Pupil detection, eye detection, eye tracking, gaze tracking, image processing, image analysis, human computer interaction.

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Fast and Accurate Pupil Isolation Based on Morphology and Active Contour

Cited by 12 publications

References 13 publications

Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization

Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization

Fast and accurate method for complete iris segmentation with active contour and morphology

PDIF: Pupil Detection After Isolation and Fitting

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