Iris Segmentation and Detection System for Human Recognition Using Canny Detection Algorithm
- Research Scholar, Department of Computer Applications, St.Peter’s University, Chennai.
- Professor, Department of Computer Applications, Dr.MGR University, Chennai.
- Professor, Department of Computer Applications, Velammal Engineering College, Chennai.
- Teaching Fellow, Department of EEE, University College of Engineering, Panruti.
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Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems to avoid future fraudulent use. Iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm. In this paper, normalization segmentation is done with canny segmentation technique. Descriptive statistical analysis of different feature detection operators is performed; many features are extracted, encoded and for classification hamming distance as a matching algorithm is used. Detection of edges may help the image for image segmentation, normalization, data compression. Here we are seeing various edge detection techniques. On comparing them we can see that canny edge detector performs better than all other edge detectors on various aspects such as give better results for noisy image, remove streaking problem & adaptive in nature etc. Using minimum number of Curvelets coefficients, we can get up to 100 % accuracy and the time consumption of the system is also very low to identify iris. The Implementation and iris detection has given better results.
[Anandhi, M. S. Josephine, V. Jeyabalaraja, S. Satthiyaraj (2015); Iris Segmentation and Detection System for Human Recognition Using Canny Detection Algorithm Int. J. of Adv. Res. 3 (Jul). 1111-1119] (ISSN 2320-5407). www.journalijar.com
