An Iris localization method for noisy infrared iris images

Kumar, Vineet; Asati, Abhijit; Gupta, Anu

doi:10.1109/icsipa.2015.7412191

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…where G σ (r) is the Gaussian smoothing function with a scale of σ; * represents convolution; r, x 0 , y 0 are the integration radius and center, respectively; I (x, y) is the original image of iris. Existing methods generally use bilinear interpolation [13] or morphological methods [9] to avoid the interference of reflective noise. However, the essence of bilinear interpolation is to select the average grayscale value of four pixels in the vertical and horizontal directions according to a fixed distance to replace the current pixel, so it is susceptible to the interpolation distance when performing reflective noise filtering.…”

Section: Reflective Noise Removalmentioning

confidence: 99%

“…A morphological method was used in previous studies [8,9] to remove the reflective noise in the image before binarizing the image. Kumar et al [8] assumed that the pupil and iris were located in the center of the image, filtered out the binary pixels close to the boundary directly to obtain the largest connected area, then estimated the center of the pupil, and used IDO to determine the iris boundaries finally.…”

Section: Introductionmentioning

confidence: 99%

“…ey [9] also performed edge detection on binary images, used CHT to locate the inner boundary of the iris, and a limited-angle IDO was used to locate the outer boundary according to the center of the inner boundary. However, method in this study [9] did not consider eyelashes noise.…”

Section: Introductionmentioning

confidence: 99%

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Iris Localization Algorithm based on Effective Area

Zhang

Wang

2021

International Journal of Antennas and Propagation

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Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

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Section: Reflective Noise Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iris Localization Algorithm based on Effective Area

Zhang

Wang

2021

International Journal of Antennas and Propagation

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“…There are two major approaches for iris segmentation; Daugman's integrodifferential operator and Hough's transform [12], [13]. Daugman's operator is the most common method in iris recognition [14].…”

Section: Introductionmentioning

confidence: 99%

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

Qasmieh

Alquran

Alqudah

2021

IJECE

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A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability.

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“…Iris localisation deals with detecting iris's inner and outer boundaries in iris biometric systems. Most of the iris localisation techniques are based on circle Hough transform (CHT) that detects circles in the images [1, 2]. The iris localisation is the slowest process in the iris biometric systems that do not have too big databases [3, 4]; moreover, it may not meet real‐time performance in embedded iris biometrics, where low‐speed central processing units (CPUs) are used.…”

Section: Introductionmentioning

confidence: 99%

Memory‐efficient architecture of circle Hough transform and its FPGA implementation for iris localisation

Kumar¹,

Asati²,

Gupta³

2018

IET image process

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This study presents a circle Hough transform (CHT) architecture that provides memory reduction between 74 and 93% without and with little degradation in the accuracy, respectively. For an image of P × Q pixels, the standard (direct) CHT requires a two‐dimensional (2D) accumulator array of P × Q cells, but the proposed CHT uses a 2D accumulator array of (P/m) × (Q/n) cells for coarse circle detection and two 1D accumulator arrays of P × 1 and Q × 1 cells for fine detection, therein reducing the memory by a factor of m × n (approximately). The proposed CHT architecture was applied to iris localisation application and carried out its comprehensive evaluation. The average accuracy of the proposed CHT for iris localisation (inner plus outer iris‐circle detection) is 98% with memory reduction of 87% compared with the direct CHT. The proposed CHT architecture was implemented on field programmable logic array targeting Xilinx Zynq device. The proposed CHT hardware takes processing time of 6.25 ms (average) for iris localisation in an image of 320 × 240 px2. The proposed work is compared with the previous work, which shows improved results. Finally, the effect of additive Gaussian noise on the CHT performance is investigated.

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An Iris localization method for noisy infrared iris images

Cited by 6 publications

References 15 publications

Iris Localization Algorithm based on Effective Area

Iris Localization Algorithm based on Effective Area

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

Memory‐efficient architecture of circle Hough transform and its FPGA implementation for iris localisation

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