Noise reduction in iris recognition using multiple thresholding

Dehkordi, Arezou Banitalebi; Abu-Bakar, S. A. R.

doi:10.1109/icsipa.2013.6707992

Cited by 11 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Type of noise Proposed method Nixon and Aguado [65] Noise Median filter (MF) Huang et al [66] Blurriness Kernel estimation of the blur Lee et al [67] Reflections Line intensity profile (LIP) Santos and Hoyle [68] Low lighting (low illumination) CLAHE method Dehkordi and Abu-Bakar [69] Noise such as eyelids, eyelash, and light reflections and pupil pixels Multiple thresholding method Liu et al [70] Poor performance accuracy of low-resolution (LR) iris recognition A heterogeneous metric learning algorithm Raffei et al [71] Low contrast Adaptive histogram equalization Bakshi et al [72] Occlusions due to eyelashes and eyelids Gaussian filters and the Hough line detector Kumar et al [73] e difference between brighter and darker pixels Top-hat and bottom-hat filters Baqar et al [74] Specular highlight due to reflection 2D linear interpolation Djoumessi [75] Occlusions due to eyelids Hough line detector Gangwar et al [76] Specular reflection reshold Radman et al [77] Reflections Morphological-retinex method Ribeiro et al [78] Low resolution and quality of iris images Stacked autoencoder (SAE) technique and convolutional neural network (CNN) technique Arsenovic et al [79] Shadows on the eye images Histogram normalization Gad et al [80] Specular reflection Morphological operations Susitha and Subban [81] Low quality due to poor contrast CLAHE method Das and Derakhshani [82] Poor contrast and brightness BPDFHE method Donida et al [83] Specular reflections and noise Inpainting algorithm and Gaussian-based bilateral filter Complexity the iris image becomes more difficult due to different noise types such as the off-angle imaging, blurring, occlusion caused by eyelids and eyelashes, and specular reflection from illumination [104]. Raffei et al [105] proposed a fusion technique which consists of three substages.…”

Section: Sourcementioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Sourcementioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

View full text Add to dashboard Cite

show abstract

“…Note that the work of Dehkordi and Abu‐Bakar 18 proposes reducing the effects of blur, occlusion due to eyelids, and pupil dilation (minimal dilation) present in the input iris image for 2D identification by using a multiple thresholding technique. In contrast, our 3D reconstruction algorithm proposed in this paper does not require additional techniques for handling these distortions.…”

Section: D Iris Synthesismentioning

confidence: 99%

3D iris model and reader for iris identification

Cohen

Sowmithran

2020

Concurrency and Computation

View full text Add to dashboard Cite

In the presence of eyelids and eyelashes movement, pupil dilation, poor lighting, blur due to movement during iris image acquisition, and factors that collectively cause distortion in the iris image, 2D image-based iris identification techniques become limited and might lead to iris misclassification due to the dependency on the image appearance (texture) on the possibly distorted image. To alleviate this problem, we introduce a new 3D iris model and reader based upon which iris identification is performed. Using a set of at least two 2D images taken from different views, a small set of reliable and corresponding salient fiducial points (corner points taken from crypts, corona, and serpentine rings of iris pattern) in the two images are extracted, from which a set of 3D iris salient points are obtained using triangulation. Corresponding salient points in the 2D images are found using the Random Sampling Consensus (RANSAC) algorithm, which is robust in identifying the inlier points that correspond to each other in different views of the iris. From this small and reliable salient point set, a denser (high-resolution) set of extra salient feature points is constructed at minimum cost using a loop subdivision method that yields corresponding extra salient points in the two images from which a high-resolution 3D iris model is obtained. The points associated with the 3D high-resolution model are tessellated to form a high-resolution triangular mesh with the appearance of a triangular patch in the image imported onto the iris personalized 3D model rendering it a 3D iris surface with appearance. This 3D model construction method allows for a 3D classification of a test iris to one of many possible irises stored in the database. The classification is based on a geometric 3D point cloud error. For cross-validation, the geometric-based classification can be augmented by considering a mean squared error (MSE) based on the appearance of corresponding points on the test and base models to further disambiguate between irises with close geometric error values.

show abstract

Iris Identification in 3D

Cohen

Sowmithran

2019

Image Analysis

View full text Add to dashboard Cite

Noise reduction in iris recognition using multiple thresholding

Cited by 11 publications

References 10 publications

Iris Recognition Development Techniques: A Comprehensive Review

Iris Recognition Development Techniques: A Comprehensive Review

3D iris model and reader for iris identification

Iris Identification in 3D

Contact Info

Product

Resources

About