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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.
Abstract: The security of biometric systems, especially pro-tecting the templates stored in the gallery database, is a primary concern for researchers. This paper presents a novel framework using an ensemble of deep neural networks to protect biometric features stored as a template. The proposed ensemble chooses two state-of-the-art CNN architectures i.e., ResNet and DenseNet as base models for training. While training, the pre-trained weights enable the learning algorithm to converge faster. The weights obtained through the base model is further used to train other compatible models, generating a fine-tuned model. Thus, four fine-tuned models are prepared, and their learning are fused to form an ensemble named as PlexNet. To analyze biometric templates’ security, the rigorous learning of ensemble is collected using a smart box i.e., application programming interface (API). The API is robust and correctly identifies the query image without referring to a template database. Thus, the proposed framework excludes the templates from database and performed predictions based on learning that is irrevocable.
Ashutosh Singh, Ranjeet Srivastva and Yogendra Narain Singh, “PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120436
@article{Singh2021,
title = {PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120436},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120436},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Ashutosh Singh and Ranjeet Srivastva and Yogendra Narain Singh}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.