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Feature Matching in Iris Recognition System using MATLAB

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@article{IJASEIT2765,
   author = {Imran Naguru and Narendra Kumar Rao B},
   title = {Feature Matching in Iris Recognition System using MATLAB},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1748--1757},
   keywords = {iris recognition; biometric identification; feature matching; iris normalization; image acquisition feature encoding; iris segmentation.},
   abstract = {Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2765},
   doi = {10.18517/ijaseit.7.5.2765}
}

EndNote

%A Naguru, Imran
%A Kumar Rao B, Narendra
%D 2017
%T Feature Matching in Iris Recognition System using MATLAB
%B 2017
%9 iris recognition; biometric identification; feature matching; iris normalization; image acquisition feature encoding; iris segmentation.
%! Feature Matching in Iris Recognition System using MATLAB
%K iris recognition; biometric identification; feature matching; iris normalization; image acquisition feature encoding; iris segmentation.
%X Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2765
%R doi:10.18517/ijaseit.7.5.2765
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 5
%@ 2088-5334

IEEE

Imran Naguru and Narendra Kumar Rao B,"Feature Matching in Iris Recognition System using MATLAB," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1748-1757, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.2765.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Naguru, Imran
AU  - Kumar Rao B, Narendra
PY  - 2017
TI  - Feature Matching in Iris Recognition System using MATLAB
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1748
EP  - 1757
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - iris recognition; biometric identification; feature matching; iris normalization; image acquisition feature encoding; iris segmentation.
N2  - Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2765
DO  - 10.18517/ijaseit.7.5.2765

RefWorks

RT Journal Article
ID 2765
A1 Naguru, Imran
A1 Kumar Rao B, Narendra
T1 Feature Matching in Iris Recognition System using MATLAB
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1748
OP 1757
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 iris recognition; biometric identification; feature matching; iris normalization; image acquisition feature encoding; iris segmentation.
AB Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2765
DO  - 10.18517/ijaseit.7.5.2765