Improvement to libor masek algorithm of template matching method for iris recognition

Kulkarni, S.B.; Hegadi, Ravindra S.; Kulkarni, Umakant P.

doi:10.1145/1980022.1980303

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…In iris recognition, Kulkarni [21], stated that ED would calculate the dissimilarity value of the two iris template generated from feature extraction process. If the two iris template comes from the same individual, the value will be fall in the range lower than the threshold, but in ED their result will never be equal to 0 even the iris comes from the same person or same side of the eye.…”

Section: B Euclidean Distancementioning

confidence: 99%

“…The equation in this technique is referred to the Pythagoras theorem. ED calculates the similarity value of the two iris template generated from feature extraction process [21].…”

Section: Introductionmentioning

confidence: 99%

“…To measure the newly proposed method, Manisha and Sanjay [25] implemented ED to measure the disagreed of bits when comparing the sample template with database template. Kulkarni [21] had compared the performance of ED with the Libor Masek Algorithm and concluded that Libor Masek had produced higher accuracy. This is because they consider the non-significant value that will raise the number of bit patterns.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Kasim

Hassan

Mohd

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Biometric recognition is gaining attention as most of the organization is seeking for a more secure verification method for user access and other security application. There are a lot of biometric systems that exist which are iris, hand geometry, and fingerprint recognition. In the biometric system, iris recognition is marked as one of the most reliable and accurate biometric in term of identification. However, the performance of iris recognition is still doubted whether the iris recognition can generate higher accuracy when involving twin iris data. So, specific research by using twin data only needs to be done to measure the performance of recognition. Besides that, a comparative study is carried out using two template matching technique which are Hamming Distance and Euclidean Distance to measure the dissimilarity between the two iris template. From the comparison of the technique, better template matching technique also can be determined. The experimental results showed that iris recognition could distinguish twin as it can distinguish two different, unrelated people as a result obtained showed the good separation between intra and interclass and both techniques managed to obtain high accuracy. From the comparison of template matching technique, Hamming Distance is chosen as a better technique with low False Rejection Rate, low False Acceptance Rate and high Total Success Rate with the value of 2.5%, 8.75%, and 96.48% respectively.

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Section: B Euclidean Distancementioning

confidence: 99%

“…The equation in this technique is referred to the Pythagoras theorem. ED calculates the similarity value of the two iris template generated from feature extraction process [21].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Kasim

Hassan

Mohd

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…There are four main stages of an iris recognition and compression system. They are, image preprocessing, feature extraction and template matching [43], and compression algorithm.…”

Section: Iris Image Preprocessingmentioning

confidence: 99%

Iris Compression and Recognition using Spherical Geometry Image

Ramadan¹

2015

ijarai

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Abstract-this research is considered to be a research to attract attention to the 3D iris compression to store the database of the iris. Actually, the 3D iris database cannot be found and in trying to solve this problem 2D iris database images are converted to 3D images just to implement the compression techniques used in 3D domain to test it and give an approximation results or to focus on this new direction in research. In this research a fully automated 3D iris compression and recognition system is presented. We use spherical based wavelet coefficients for efficient representation of the 3D iris. The spherical wavelet transformation is used to decompose the iris image into multi-resolution sub images. The representation of features based on spherical wavelet parameterization of the iris image was proposed for the 3D iris compression system. To evaluate the performance of the proposed approach, experiments were performed on the CASIA Iris database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the iris image was found to generate good recognition results.

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