PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection

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.

@article{Singh2021,
  title     = {PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {4},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Ashutosh Singh and Ranjeet Srivastva and Yogendra Narain Singh},
  doi       = {10.14569/IJACSA.2021.0120436},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120436}
}