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  Published Paper Details:

  Paper Title

IRIS-BASED PERSONAL VISION THROUGH IN-DEPTH LEARNING METHODS

  Authors

  R.Rami Reddy,  Dr.T.Manikumar

  Keywords

Iris-based human identity recognition, CNN, Transfer learning, Image segmentation, artificial neural networks.

  Abstract


One of the most important modules in a computer program is the one that handles user security [2] [5]. It was proven that simple passwords can not guarantee high efficiency and are easily accessible by hackers [1] [3]. Another well-known method is biometric based analysis. In recent years, increased interest has been seen in the iris as a biometrics object. It was due to the high efficiency and accuracy guaranteed by this measurable feature [15] [13]. The results of such interest can be seen in the literature. There are many, many different methods proposed by different authors [14] [9]. In this paper, the authors introduce their own identity-based algorithm based on the iris [6]. To differentiate, the algorithm used a CNN (MobileNet)-based transfer learning model and neural networking networks. Immediately after the separation, a separation is performed on the separated outlet and part of the iris is separated. Studies have shown that satisfactory results can be achieved in the proposed way [4] [11]

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2205827

  Paper ID - 220698

  Page Number(s) - h133-h138

  Pubished in - Volume 10 | Issue 5 | May 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  R.Rami Reddy,  Dr.T.Manikumar,   "IRIS-BASED PERSONAL VISION THROUGH IN-DEPTH LEARNING METHODS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 5, pp.h133-h138, May 2022, Available at :http://www.ijcrt.org/papers/IJCRT2205827.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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