• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.181-183
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• 2004

In this paper, We proposed an algorithm which extraction iris region from 2D image. Our method is composed of three parts : internal boundary defection and external boundary detection. Since eyelid and eyelash cover part of the boundary and the size of iris changes continuously, it is difficult to extract iris region accurately. For the interior and exterior boundary detection, we used partial differentiation of histogram. Performance of the proposed algorithm is tested and evaluated using 360 iris image samples.

• 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).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.48-60
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• 2007

In previous researches for iris feature extraction, they transform a original iris image into rectangular one by stretching and interpolation, which causes the distortion of iris patterns. Consequently, it reduce iris recognition accuracy. So we are propose the method that extracts iris feature by using polar coordinates without distortion of iris patterns. Our proposed method has three strengths compared with previous researches. First, we extract iris feature directly from polar coordinate circular iris image. Though it requires a little more processing time, there is no degradation of accuracy for iris recognition and we compares the recognition performance of polar coordinate to rectangular type using by Hamming Distance, Cosine Distance and Euclidean Distance. Second, in general, the center position of pupil is different from that of iris due to camera angle, head position and gaze direction of user. So, we propose the method of iris feature detection based on polar coordinate circular iris region, which uses pupil and iris position and radius at the same time. Third, we overcome override point from iris patterns by using polar coordinates circular method. each overlapped point would be extracted from the same position of iris region. To overcome such problem, we modify Gabor filter's size and frequency on first track in order to consider low frequency iris patterns caused by overlapped points. Experimental results showed that EER is 0.29%, d' is 5,9 and EER is 0.16%, d' is 6,4 in case of using conventional rectangular image and proposed method, respectively.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.898-905
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• 2005

Iris recognition Is that identifies a user based on the unique iris muscle patterns which has the functionalities of dilating or contracting pupil region. Because it is reported that iris recognition is more accurate than other biometries such as face, fingerprint, vein and speaker recognition, iris recognition is widely used in the high security application domain. However, if unnecessary information such as eyelid and eyelash is included in iris region, the error for iris recognition is increased, consequently. In detail, if iris region is used to generate iris code including eyelash and eyelid, the iris codes are also changed and the error rate is increased. To overcome such problem, we propose the method of detecting eyelid by using pyramid searching parabolic deformable template. In addition, we detect the eyelash by using the eyelash mask. Experimental results show that EER(Equal Error Rate) for iris recognition using the proposed algorithm is lessened as much as $0.3\%$ compared to that not using it.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.876-884
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• 2003

It is essential to identify both the pupil and iris boundaries for iris recognition. The circular edge detector proposed by Daugman is the most common and powerful method for the iris region extraction. The method is accurate but requires lots of computational time since it is based on the exhaustive search. Some heuristic methods have been proposed to reduce the computational time, but they are not as accurate as that of Daugman. In this paper, we propose a pupil and iris boundary finding algorithm which is faster than and as accurate as that of Daugman. The proposed algorithm searches the boundaries using the Daugman's circular edge detector, but reduces the search region using the problem domain knowledge. In order to find the pupil boundary, the search region is restricted in the maximum and minimum bounding circles in which the pupil resides. The bounding circles are obtained from the binarized pupil image. Two iris boundary points are obtained from the horizontal line passing through the center of the pupil region obtained above. These initial boundary points, together with the pupil point comprise two bounding circles. The iris boundary is searched in this bounding circles. Experiments show that the proposed algorithm is faster than that of Daugman and more accurate than the conventional heuristic methods.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.233-238
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• 2005

With iris recognition algorithm, unique iris code can be generated and user can be identified by using iris pattern. However, if unnecessary information such as eyelash is included in iris region, the error for iris recognition is increased, consequently. In detail, if iris region is used to generate ins code not excluding eyelash and the position of eyelash is moved, the iris codes are also changed and the error rate is increased. To overcome such problem, we propose the method of detecting eyelash by using mask and excluding the detected eyelash region in case of generating iris code. Experimental results show that EER(Equal Error Rate) for iris recognition using the proposed algorithm is lessened as much as $0.18\%$ compared to that not using it.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.121-130
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• 2005

In this paper, we propose a feature extraction method for iris recognition using wavelet transforms. The wavelet transform is fast and has a good localization characteristic. In particular, the low frequency band can be used as an effective feature vector. In iris recognition, the noise caused by eyelid the eyebrow, glint, etc may be included in iris. The iris pattern is distorted by noises by itself, and a feature extraction algorithm based on filter such as Wavelets, Gabor transform spreads noises into whole iris region. Namely, such noises degrade the performance of iris recognition systems a major problem. This kind of noise has adverse effect on performance. In order to solve these problems, we propose to divide the iris image into a number of sub-region and apply the wavelet transform to each sub-region. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform and region division noticeably improves recognition performance. However, it is noted that the processing time of the wavelet transform is much faster than that of the existing methods.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.178-186
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• 2003

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.643-650
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• 2013

In this paper, we propose a novel method for enhancing the detection speed and rate by reducing the computation in Hough Circle Transform on real-time iris detection of smartphone camera image. First of all, we find a face and eyes from input image to detect iris and normalize the iris region into fixed size to prevent variation of size for iris region according to distance from camera lens. Moreover, we carry out histogram equalization to get regular image in bright and dark illumination from smartphone and calculate minimal iris range that contains iris with the distance between corner of the left eye and corner of the right eye on the image. Subsequently, we can minimize the computation of iris detection by applying Hough Circle Transform on the range including the iris only. The experiment is carried out in two case with bright and dark illumination. Our proposed method represents that detection speed is 40% faster and detection rate is 14% better than existing methods.