Biometric personal identification based on iris patterns

Zhu, Yong; Tan, Tieniu; Wang, Yunlong

doi:10.1109/icpr.2000.906197

Cited by 148 publications

(13 citation statements)

References 5 publications

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“…Table 3 shows the recognition accuracy in the form of the EER of various algorithms. The proposed method is compared with other popular enhancement techniques, including Histogram Equalization, used in [51] and [52], Histogram Stretching, in [51], simple Bi-linear Interpolation, Bi-cubic interpolation, and combined bilinear and un-sharp masking techniques, which are usually used for the enhancement and restoration of iris images. The results show that in all cases the proposed method outperformed all other popular enhancement algorithms.…”

Section: Evaluation Approachmentioning

confidence: 99%

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Sajjad¹,

Ahn²,

Jung³

2016

KSII TIIS

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Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

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Section: Evaluation Approachmentioning

confidence: 99%

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Sajjad¹,

Ahn²,

Jung³

2016

KSII TIIS

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“…The algorithm was based on textures analysis using multichannel Gabor filtering with the Wavelet transform. The authors indicated that their method was invariant to scale, rotation, and translation [7]. In 2003, Masek proposed an open-source iris recognition system.…”

Section: Introductionmentioning

confidence: 99%

Iris biometric recognition of latin american eyes in a white light controlled environment

Acosta¹,

Zapata

Osorio

2020

Sci. tech

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Iris biometric recognition has taken a great interest in the last decade. Many private and public facilities need to improve their security access due to the increasing terrorist attacks, the growing number of fraud and identity theft among other felonies. The regular technologies are almost all based on infrared light illumination due to the ease of using this for texture approach recognition. Even Though this technique is suitable in most cases, the highest price of these cameras on the market demands the research of either daylight or a white light solution. There are some known iris databases such as CASIA and UBIRIS, but they are not from Latin-American eyes that have different occlusion and color issues. A white light controlled environment was used to take the set of pictures 500 that let us have a new color database to carry out the experiments based on the EigenIris technique. The best iris and pupil segmentations were obtained either on the red or green color layers of the RGB images at 100% of correct localization. In this research, a sensitivity of 0,82 and specificity of 0,85 was achieved with a modified version of the EigenIris method using the blue color layer.

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“…Then the radius of the circle was normalized with respect to the radius of the iris, ensuring that the same points of the texture of the iris sampled according to size of the iris in the picture. Zhu et al [7] have done comparative studies between wavelets non-orthogonal Gabor and orthogonal wavelet of Daubechies of order 4 ("db4"). In this method, Gabor wavelets multidirectional and different frequencies were used to separate the texture of the iris in different frequency sub-bands.…”

Section: Introductionmentioning

confidence: 99%

Efficient Iris Pattern Recognition Method by using Adaptive Hamming Distance and 1D Log-Gabor Filter

Tobji¹,

Wu²,

Ayoub³

et al. 2018

ijacsa

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Iris recognition is one of the highly reliable security methods as compared to the other bio-metric security techniques. The iris is an internal organ whose texture is randomly determined during embryonic gestation and is amenable with a computerized machine vision system for the remote examination. Previously, researchers utilized different approaches like Hamming Distance in their iris recognition algorithms. In this paper, we propose a new method to improve the performance of the iris recognition matching system. Firstly, 1D Log-Gabor Filter is used to encode the unique features of iris into the binary template. The efficiency of the algorithm can be increased by taking into account the coincidence fragile bit's location with 1D Log-Gabor filter. Secondly, Adaptive Hamming Distance is used to examine the affinity of two templates. The main steps of proposed iris recognition algorithm are segmentation by using the Hough's circular transformation method, normalization by Daugman's rubber sheet model that provides a high percentage of accuracy, feature encoding and matching. Simulation studies are made to test the validity of the proposed algorithm. The results obtained ensure the superior performance of our algorithm against several state-of-the-art iris matching algorithms. Experiments are performed on the CASIA V1.0 iris database, the success of the proposed method with a genuine acceptance rate is 99.92%.

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Biometric personal identification based on iris patterns

Cited by 148 publications

References 5 publications

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Iris biometric recognition of latin american eyes in a white light controlled environment

Efficient Iris Pattern Recognition Method by using Adaptive Hamming Distance and 1D Log-Gabor Filter

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