Implementation of Fusion of Sclera and Periocular As A Biometric Authentication System Using Deep Learning
How to Cite?
V. Sandhya, Nagaratna P Hegde, "Implementation of Fusion of Sclera and Periocular As A Biometric Authentication System Using Deep Learning," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 212-221, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P224
In today’s digitized world, individual authentication has become a customary procedure for all organizations to provide access to their stakeholders. The type of authentication has been changing from uni-modal such as a fingerprint, to multi-modal as face and fingerprint, for identifying an individual. A multi-model authentication system differs with respect to the number of times the input is captured from the user, user cooperation, willingness, and accuracy. In this proposed system, input is captured only once. The sclera and periocular features are extracted as patches from the image. The model is trained using image patches of different sizes using a deep neural network system. The resultant accuracy of the proposed system is efficient when compared with the existing multi-modal systems.
Bio-Metric, Sclera, Periocular, Multi-Model, Image Patches, CNN, deep learning.
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