• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.35-40
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• 2019

In this paper, we propose a technique to reconstruct the iris image from the iris code by analyzing the process of generating the iris code and calculating it inversely. Iris recognition is an authentication method for authenticating an individual's identity by using iris information of an eye having unique information of an individual. The iris recognition extracts the features of the iris from the iris image, creates the iris code, and determines whether to authenticate using the corresponding code. The iris recognition method using the iris code is a method proposed by Daugman for the first time and is widely used as a representative method of iris recognition technology currently used commercially. In this paper, we restore the iris image with only the iris code, and test whether the reconstructed image and the original image can be recognized, and analyze restoration vulnerability of Daugman's iris code.

• Journal of Environmental Science International
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• v.14 no.3
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• pp.265-269
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• 2005

This study was carried out to provide the basic data for an identifying system for Iris species distributed in Korean market from complete analysing of genetic relationship between three native Iris species and one cultivar bred from the native Iris plant. RAPD analysis of genetic relationship among 4 Irises was possible. According to the RAPD analysis, they were divided into two groups. Among 4 Irises used in this study, Iris laevigata 'Veriegata', Iris laevigata and Iris setosa were classified into the same group since they had many similarities even though the habitat of Iris laevigata in Korean peninsular is restricted mainly in the south and Iris setosa is naturally inhabited in the northern part of Kangwondo. The value for the dissimilarity index of Iris laevigata and Iris laevigata 'Veriegata' was 6.757. The value for the dissimilarity index of Iris laevigata and Iris dichotoma was 95.000, so that they were genetically the farthest among them since the genetic relationship between two species are separated far if the value of the dissimilarity index is close to 100.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

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).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.117-137
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• 2010

Iris recognition is a biometric technique which uses unique iris patterns between the pupil and sclera. The advantage of iris recognition lies in high recognition accuracy; however, for good performance, it requires the diameter of the iris to be greater than 200 pixels in an input image. So, a conventional iris system uses a camera with a costly and bulky zoom lens. To overcome this problem, we propose a new method to restore a low resolution iris image into a high resolution image using a single image. This study has three novelties compared to previous works: (i) To obtain a high resolution iris image, we only use a single iris image. This can solve the problems of conventional restoration methods with multiple images, which need considerable processing time for image capturing and registration. (ii) By using bilinear interpolation and a constrained least squares (CLS) filter based on the degradation model, we obtain a high resolution iris image with high recognition performance at fast speed. (iii) We select the optimized parameters of the CLS filter and degradation model according to the zoom factor of the image in terms of recognition accuracy. Experimental results showed that the accuracy of iris recognition was enhanced using the proposed method.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.321-322
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• 2006

In this paper, we propose a method that extracts invariant iris codes from user's iris pattern in order to apply these codes to a new cryptographic construct called fuzzy vault. The fuzzy vault, proposed by Juels and Sudan, has been used to manage cryptographic key safely by merging with biometrics. Generally, iris data has intra-variation of iris pattern according to sensed environmental changes, but cryptography requires correctness. Therefore, to combine iris data and fuzzy vault, we have to extract an invariant iris feature from iris pattern. In this paper, we obtain invariant iris codes by clustering iris features extracted by independent component analysis(ICA) transform. From experimental results, we proved that the iris codes extracted by our method are invariant to sensed environmental changes and can be used in fuzzy vault.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.33-39
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• 2018

In this paper, we propose a describes how to detect a false iris image by inserting watermark into a iris image. The existing method, which inserts the watermark into the entire iris image to detect a fake iris, has a problem that can evade it by segmenting iris region of an iris image. The purpose of overcoming the problem, this paper proposes a new fake iris detection technique based on digital watermark. It first searches a central point of an iris image, divide the image into blocks with respect to the point. executes Discrete Cosine Transform, inserts watermark into the blocks, and then verifies an iris image using NC(Normalized Correlation). In the experiments, we confirm the robustness for attacks - crop and JPEG.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.23-26
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• 2000

In this paper, to perform iris recognition, the iris is changed to 1-D iris signature and methods of efficient iris pattern transformation are discussed. To represent iris signature's frequency characteristics, Fourier transform, Gabor filtering, and wavelet transform are proposed. The consistency between same person's iris and the discrimination between different person's iris are defined by using correlation. Based on these, three transform methods are compared and analyzed.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.287-288
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• 2007

In this paper, we propose a fake-iris detection method. In order to detect the fake-iris, we measure physiological features which are the reflectance ratio of the iris to the sclera at 750 nm and that at 850nm. In order to classify live and fake iris features, we use support vector data description (SVDD). From our experimental results, it is clear that our fake-iris detection method achieves high performance when distinguishing between a live-iris and a fake-iris.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.351-354
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• 2005

In this paper, we propose a new fake-iris detection method. In order to detect the fake-iris, we measure the change of the reflectance ratio between iris and sclera. In a live iris, the amount of change is relatively large compared to that in a fake-iris. From our experimental results, it is clear that our fake-iris detection method achieves high performance when distinguishing between a live-iris and a fake-iris.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.30-39
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• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.