Deep Learning Iris Recognition Method Based on Capsule Network Architecture
Kavya. C1, Divya. G2

1C.Kavya sree*,Department of Computer Scienceand Engineering, ,Saveetha school of engineering ,Saveetha Institute of Medical and Technical Sciences,Chennai, India.
2G.Divya,Assistant Professor, Department of Computer Science and Engineering, Saveetha school of engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 668-671 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7236038620/2020©BEIESP | DOI: 10.35940/ijrte.F7236.038620

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Abstract: In biometric acknowledgment, which is generally utilized in different fields. As of late, numerous profound learning strategies have been utilized in biometric acknowledgment, attributable to their points of interest. In this deep learning process we adjust the existing network structure and providing the modified routing algorithm technique which is depends on dynamic routing between two capsule layers. This layers helps to maintain and adopt a iris recognition. Various iris data sets are used for recognition. These datasets are trained and tested with the help of different pupil size of an iris. In order to show the recognition ability when the environment varies. The test of dataset achieves 96.2%. CASIA-V4 Lamp dataset gives the highest accuracy of 98.34%. It shows the apply of capsule network in iris recognition.
Keywords: Deep Learning Algorithms, Capsule Network Architecture, Iris Reorganization.
Scope of the Article: Deep Learning.