Abstract
The necessity of computer security is been evolving day to day. In the current generation, biometrics are one of the efficient ways to achieve security. At the same time, many spoofing algorithms were also came into existence using which biometrics like fingerprints can easily be cracked. Face recognition mechanisms are being widely used in the current scenario as they are dependable and they can be implemented easily. The major disadvantage with the current face recognition algorithms is, a person’s device can be unlocked with his/her twin who has same facial traits. The best alternative is identity recognition based on the iris. In this study, the authors present their individual method for recognizing human identity based on iris recognition. For classification, CNN-based transfer learning model (Mobile Net) is used. Primarily, the classification is done based on which the identity of a particular person will be recognized.
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Acknowledgements
We would like to thank for the contributions in understanding the implementation of concepts of iris-based human identity recognition using CNN through https://d-nb.info/1233275992/34 in this project. Dr. N. Leelavathy for her invaluable suggestions which led to improving the quality of this paper.
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Karthik, C., Sujatha, B., Charan Babu, T.K., PhaniKumar, D., Mohan Krishna, S. (2023). Iris-Based Human Identity Recognition Using Transfer Learning Approach. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_27
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DOI: https://doi.org/10.1007/978-981-99-6706-3_27
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