Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

Yahiaoui, Meriem; Monfrini, Emmanuel; Dorizzi, Bernadette

doi:10.1016/j.patrec.2016.05.025

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…en, by using a dilation morphological operation, pixels are further added close to the borders of the important iris region to enlarge the selected regions. It is noted that the occlusion of the iris region due to eyelashes and eyelids was still prevalent, leading Yahiaoui et al to further develop the method by applying a CHT and localizing the pupil region using the Canny edge map [89]. In order to preserve the robust edges and to overcome the sensitivity of the edge detection algorithm, they applied an anisotropic filter followed by an adaptive threshold.…”

Section: Hough Transform Methodmentioning

confidence: 99%

“…To develop the performance of iris segmentation of the OSIRISV4 open-source model for these challenges, Yahiaoui et al [89] introduced a method by extending the Viterbi algorithm [140] by adding statistical techniques for iris segmentation based on unsupervised approaches, and they focused especially on the hidden Markov chain method as suggested in [89,141,142]. To increase the performance rate of the segmentation phase of iris images, some limitations were still prevalent such as reflection from glasses and different scanners as well as reflection occluded by eyelids and eyelashes, leading He et al [143] and Liu et al [35] to further develop the Hough transform (HT) method based on the edge detection method which was applied by Canny [144] to segment the iris region.…”

Section: Histogram-and Contour-based Segmentationmentioning

confidence: 99%

“…To normalize the iris region, Umer et al [56], Yahiaoui et al [89], and Al-Waisy et al [111] used a rubber-sheet model [16] to change the segmented iris from a circle shape into a rectangular shape. e rubber-sheet model transforms the linear pattern of the iris area from Cartesian to polar coordinates.…”

Section: Normalization Techniquesmentioning

confidence: 99%

“…Other researchers such as Yahiaoui et al [89], Othman et al [138], Jan et al [214,215], Jan and Usman [216], Raja et al [217], and, more recently, Nazmdeh et al [6] proposed the use of 2D-Gabor filters, which are very popular and have been successfully implemented in practical applications. 2D-Gabor filters are typically used to obtain the global tissue information in iris regions for the feature extraction method.…”

Section: Feature Extraction Using Traditional Techniquesmentioning

confidence: 99%

See 3 more Smart Citations

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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Section: Hough Transform Methodmentioning

confidence: 99%

Section: Histogram-and Contour-based Segmentationmentioning

confidence: 99%

Section: Normalization Techniquesmentioning

confidence: 99%

Section: Feature Extraction Using Traditional Techniquesmentioning

confidence: 99%

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Iris Recognition Development Techniques: A Comprehensive Review

2021

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“…Another work using the sclera for iris localization is that of Tan and Kumar [13], which utilized Zernike moments and colour features as inputs to a neural network and a Support Vector Machine (SVM) to classify iris and sclera pixels. Other approaches include those of Li and Savvides [14], who classified pixels in normalized iris maps into iris or occlusion by means of Gabor filters and Gaussian mixture models (GMMs); Yahiaoui et al [15], who applied a method based on Hidden Markov Chain on NIR iris images; and Radman et al [16] who developed a method based on Histogram of Oriented Gradients (HOG) over a SVM and a cellular automata evolved via the GrowCut technique. There are also methods utilizing convolutional neural networks (CNN), such as that of Liu et al [17], who implemented two different neural network architectures for iris segmentation of noisy iris images acquired at-adistance and on-the-move.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

Fuentes-Hurtado

Naranjo

Diego-Más

et al. 2019

J Image Video Proc.

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This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.

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Design and Development of Efficient Algorithms for Iris Recognition System for Different Unconstrained Environments

Prasad¹,

Manjunath

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

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Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

Cited by 12 publications

References 20 publications

Iris Recognition Development Techniques: A Comprehensive Review

Iris Recognition Development Techniques: A Comprehensive Review

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

Design and Development of Efficient Algorithms for Iris Recognition System for Different Unconstrained Environments

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