An Unsupervised Iris Based Biometric System with Inherent Feature Thresholding
Venkata Rathnam Korukonda1, E. Sreenivasa Reddy2
1VenkataRathnam Korukonda*, Research Scholar in Rayalaseema University, Kurnool, A.P, India.
2Dr. E. Sreenivasa Reddy, Professor &Dean, University college of Engineering, Acharya Nagarajuna University, Guntur, A.P, India.
Manuscript received on December 12, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1742-1753 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8717019320/2020©BEIESP | DOI: 10.35940/ijitee.C8717.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper provides a general indication of the existing approaches rely on basic factor (i.e. extraction of iris information, affine transform, and distance matrix) as input. An essential factor in effective design of IRIS based biometric approach is the accuracy with which the model can estimate a region of interest (i.e. IRIS) within constraints and unforeseen issues, which can be very problematical but need of the hour. We introduced a new IRIS based biometric system that incorporates various factors that takes complete information of eye for developing the feature set (digest) while affine transforms are not incorporated while the three sets distance measure is incepted to enhance accuracy. The algorithm-based size of template, functionality of distance measure and/or scope, methods and/or function of application through well-defined scientific and statistical principles. Unfortunately, the accuracy of the existing approaches is limited despite the large scale of experience with several improvements based on digital image processing and statistical models. Henceforth, we incorporated several texture analysis algorithms with computing techniques along with several parametric enhancement constraints to ensure the feasibility, effectiveness and efficiency of the proposed framework in comparison with the existing methods.
Keywords: Iris Biometric, Texture Analysis, Applied Statistics, Wavelet Transforms, Image Enhancement.
Scope of the Article: System Integration