A Practical Iris Recognition Algorithm

Tian, Qichuan; Liu, Zhengguang; Li, Linsheng; Sun, Zhigang

doi:10.1109/robio.2006.340208

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…In this experiment, we adopted the same method, as in Experiment 1, for the localization of the outer boundary. For the localization of the inner boundary, we located the circle that surrounds the blackest area 11. The average time for this process is 16 s. The result of this experiment is not efficient also (Figure 4).…”

Section: Experiments and Resultsmentioning

confidence: 95%

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An iris recognition model for secured access to data

Khoury¹,

Egea²,

Meskaoui³

et al. 2011

Security Comm Networks

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The exponential growth in the usage of the Internet as well as the emergence of advanced applications, increase the risk of unauthorized access to confidential information. For the last few years, biometric techniques, especially the ones related to iris recognition, have proven their reliability in providing secure access to shared resources in different domains. Many algorithms have been proposed for implementing the iris recognition biometric techniques. In this paper, we study and compare these proposals and propose an efficient new algorithm for iris recognition.

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Section: Experiments and Resultsmentioning

confidence: 95%

“…Tian et al . adopted the smallest sum of intensities of square window to detect the pupil position 11.…”

Section: Related Workmentioning

confidence: 99%

An iris recognition model for secured access to data

Khoury¹,

Egea²,

Meskaoui³

et al. 2011

Security Comm Networks

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“…In 1992, John Daugman was the first to develop the iris identification software, it has been tested for a billion images and the failure rate has been found to be very low. His systems are patented by the Iriscan Inc. and are also being commercially used in Iridian technologies, UK National Physical Lab, British Telecom etc, while since the early 1990s [4].…”

Section: Related Work and Contributionmentioning

confidence: 99%

Iris Texture Recognition Using Co-occurence Matrix Features with K_means Algorithm

Kadim

2011

JNUS

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Iris Recognition is a rapidly expanding method of biometric authentication that is well suited to be applied to any access control system requiring high level of security. In this paper k-means algorithm is employed to optimize the database enrollment, this is carried out by choosing the best image (among many) for the same person to be a template in the database. Iris images are mapped into texture features produced from co-occurrence matrix. Experimental results show that the performance of the proposed recognition system gave true identification rate of about 86% when using optimized database and 59% when using selected database.

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“…In iris feature extraction stage, iris normal region is filtered by using low frequency filter and then the texture features are extracted by using 2D zero-crossing detection operator, at last iris features are encoded into binary feature template [4].…”

Section: Related Workmentioning

confidence: 99%

Multilevel Network Security Based on Iris Biometric

Tallapragada

Rajan²

2010

2010 International Conference on Advances in Computer Engineering

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Network security and protection of data have been of great concern and a subject of research over the years. There are many different forms of cryptographic mechanisms like AES, Triple DES, MD5 proposed to guarantee data security. In a network, the success of the algorithm depends on the length of the key that user uses. It is observed that due to convenience of remembering the key, users use short keys. This increases the vulnerability of the data. In this work we propose a unique authentication and encryption technique using IRIS biometric pattern of a person. At the time of registration to the network, a person's IRIS is scanned and phase features of the image are generated using Log-Gabor filter. Filter output is 64 x 64 vectors which is difficult to use as key or to transmit over the network for authentication. Therefore a local binary code of sixty four byte is extracted of this matrix and is transmitted to the server. As this is the basis of entire algorithm functioning, we use a secured socket layer with SH1 and 128 keys(Browser supported) for this communication. Server stores this key as users Identity or password. When he wants to transfer data to another user, first his IRIS pattern is generated, it is sent to the server, server authenticates the user by comparing the incoming IRIS code with the one saved in database by using Euclidian distance. Once authenticated, user is allowed to exchange information over the network. Every information is now be encrypted using the receivers IRIS code with MD5. The success of the technique depends upon the Quality of the scan and recognition rate. There is always an error margin of 5% to 10% when IRIS code is generated which may result in an unsuccessful authentication. Therefore at the time of registration, we scan the person's eyes thrice and two codes are embedded in a smart card as reference code. This smart card is issued to the user by the administrator. At the time of authentication, user code is first compared with the code of smart card and if matched (Even with an error margin of 10%) then only his code is transmitted to server for authentication. Storing the rough key in the server is not safe as if the server data is hacked, then these keys can be easily tampered with. Hence rather than storing the rough keys, we embed them in random images using steganographic means and store the image binary in the server. Thus not only the data in the network, but also the server data is secured.

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A Practical Iris Recognition Algorithm

Cited by 8 publications

References 5 publications

An iris recognition model for secured access to data

An iris recognition model for secured access to data

Iris Texture Recognition Using Co-occurence Matrix Features with K_means Algorithm

Multilevel Network Security Based on Iris Biometric

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