Abstract:
In modern digital security systems, stochastic random processes and internal mathematical
transformations are widely used in the creation and maintenance of encryption keys, such
as passwords and PINs. Despite offering robust protection, these keys need complicated
and costly systems for distribution and storage. In order to avoid the requirement for
complex storage and distribution procedures, this study investigated a different strategy
that generates encryption keys using biometric data. For guaranteeing the security and
dependability of the generated keys, the key generation process is made to be resistant to
noise, changes, and assaults on the sensor data. A set of combined biometric face images
and fingerprint data was used for experiments that show how well and reliably the proposed
system works at making strong cryptographic keys. The study explores biometric key
generation techniques based on deep learning models, specifically Facenet and VGG19
with PCA for dimensional reduction convolutional neural networks used to extract
biometric features from human facial images and fingerprint images, respectively. We
combined the extracted features and divided them into two groups: train and test. The
developed Siamese Neural Network (SNN) model based on this dataset showed promising
results, with train and validation loss reducing from 0.35 to 0.04 and 0.3 to 0.03,
respectively. Measured using vector converter sigma similarity and sigma difference, the
accuracy reached 99.8% and 46.0%, respectively. The results indicate that the system
achieves high key generation rates while maintaining low error rates, with False
Acceptance Rate (FAR) and False Rejection Rate (FRR) less than 1% and 2.7%,
respectively. This makes it suitable for use in secure authentication systems that require
strong and reliable keys. Overall, this thesis contributes to the advancement of biometric
security systems by using a deep learning-based approach with code-based cryptography
for generating secure keys from fused biometric data.
Key words: Deep Learning, SNN model, Facenet, VGG19, PCA, Coded based
Cryptography, Biometric, and Fusion