Implementing The Secure Triplet Loss For Template Cancelebility In Iris Biometrics
Vamshi Krishna Navulla
Department of Computer Science and Engineering University at Buffalo
vnavulla@buffalo.edu
(1) Data Preprocessing
The data pre-processsing is done in the aux_functions.py, when called from the iris_prepare_AMF_inception.py. Also, the images are transformed in the dataset.py when called from the trainer scripts.
(2)Fine Tuning
Fine Tuning The models are loaded in the models.py in their respective init functions. The freeze member functions are responsible for freezing the layers.
(3) Training
Training The traning is done in respective scripts, iris_secure_Inception/iris_secure_attention for inception resnet and Vit models respectively.
(3) Results
Results The results are shown with the help of result_analysis.py, where it calls the required plotting functions from the aux_functions.py script.
This repository is forked from the parent repository, from where the majority of the code was referenced from [link], which was published as part of their paper referenced below [pdf]
J. R. Pinto, M. V. Correia, and J. S. Cardoso, "Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics", in IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(2): pp. 180-189, 2021.
[link] [pdf] [bib]
J. R. Pinto, J. S. Cardoso, and M. V. Correia, "Secure Triplet Loss for End-to-End Deep Biometrics", in 8th International Workshop on Biometrics and Forensics (IWBF 2020), 2020.
[link] [pdf] [bib]
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(3) Pinto, J.R.; Correia, M.V.; Cardoso, J.S.: J. R. Pinto, M. V. Correia, and J. S. Cardoso, "Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics". IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(2):180-189, 2021.
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