• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.415-426
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• 2021

The implementation of iris biometrics on smartphone devices has recently become an emerging research topic. As the use of iris biometrics on smartphone devices becomes more widely adopted, it is to be expected that there will be similar efforts in the research community to beat the biometric by exploring new spoofing methods and this will drive a corresponding requirement for new liveness detection methods. In this paper we addresses the problem of presentation attacks (Spoofing) against the Iris Recognition System on mobile devices and propose novel Presentation Attack Detection (PAD) method which suitable for mobile environment.

• The Journal of the Korea Contents Association
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• v.3 no.3
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• pp.47-54
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• 2003

In this paper, we implemented embedded LNUX-based UI(User Interface) board which can be applied to Human Iris recognition product. for this purpose, we also analyzed and designed LNUX operating system and adapted boot loader, kernel, control program modules according to the developed H/W architectures. As the experimental results shows that Iris recognition system is operable and embedded LNUX-based UI board which is connected to heterogeneous system by TCP/IP protocol can both successfully send and receive data this UI board has been able to obtain high performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.348-354
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• 2003

This proposes a new algorithm for advanced personal identification system using iris pattern and speech signal. Since the proposed algorithm adopts a fusion scheme to take advantage of iris recognition and speaker identification, it shows robustness for noisy environments. For evaluating the performance of the proposed scheme, we compare it with the iris pattern recognition and speaker identification respectively. In the experiments, the proposed method showed more 56.7% improvements than the iris recognition method and more 10% improvements than the speaker identification method for high quality security level. Also, in noisy environments, the proposed method showed more 30% improvements than the iris recognition method and more 60% improvements than the speaker identification method for high quality security level.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.219-224
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• 2011

Recently increasing importance of information security, personal security is researched. Among them, biometrics research is very good at recognition and security particularly in terms of iris recognition. Recent hospital physicians and employees for access control is emphasized. But most of them, easy-employee card access control systems are used. It has difficulties of iris recognition on the issue of accurate iris recognition algorithm to eliminate noise and inaccuracy of pretreatment methods for recognition from existing research. Therefore, this paper complements existing encryption methods to the disadvantages of biometric iris recognition using high-access records in the hospital management system is applied. In addition to conventional pretreatment process to increase recognition eyebrows when mask line component added to the extraction mask, the correct preparation method, and accordingly proposed to improve the recognition of records management systems offer access to the hospital.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1147-1155
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• 2005

This paper deals with Iris recognition as one of biometric techniques which is applied to identify a person using his/her behavior or congenital characteristics. The Iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D Iris pattern having a property of size invariant and using the fuzzy LDA which is further through four types of 2D Gabor wavelet. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use four different matching values obtained from four different directional Gabor wavelet and select the maximum value, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 300 Iris Patterns extracted from 50 subjects and finally got more higher than $90\%$ recognition rate.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.61-64
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• 2002

In this paper, we propose the iris feature extraction by using statistical methods. There are many approaches for iris feature extraction, but most of them require a set of parameters that one should choose for the transformation to obtain a useful representation of the iris. It would be most useful to estimate the method of the iris feature extraction from iris itself. Therefore, we apply the unsupervised statistical methods for the iris feature extraction.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.445-448
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• 2003

This paper presents a novel iris recognition method using wavelet transform and curve simplification. One-dimensional signals, which are calculated over circles on the iris, are decomposed into a multiple frequency bands. Each decomposed signal is approximated by a piecewise linear curve connecting node points. The curve is simplified by progressively removing unimportant node points while keeping the shape of the curve. Finally, a small number of node points represent features of each signal. Experiment results show that the presented method results in good performance in various noise environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.705-708
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• 2018

Among biometrics commercialized for security, iris recognition technology has the most excellent security for the probability of the match between individuals is the lowest. Current commercialized iris recognition technology has excellent recognition ability, but this technology has a fatal drawback. Without the user's active cooperation, it cannot recognize the iris correctly. To make up for this weakness, recent trend of iris recognition development mounts a non-volunteering, unconstrained method. According to this information, the objective of this research is developing a module that can identify people iris from a video acquired by high performance infrared camera in a range of 3m and in a involuntary way. For this, we import images from the video and find people's face and eye positions from the images using Haar classifier trained through Cascade training method. finally, we crop the iris by Hough circle transform and compare it with data from the database to identify people.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.281-286
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• 2003

Wavelet Transform can effectively represent the local characteristics of a signal in the space-frequency domain. However, the feature vector extracted using wavelet transform is not translation invariant. This paper describes a new feature extraction method using wavelet transform, which is translation-invariant. Based on this translation-invariant feature extraction, the iris recognition method, based on this feature extraction method, is robust to noises. Experimentally, we show that the proposed method produces super performance in iris recognition.