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| dc.contributor.author | TAHAYU, GIZACHEW YIRGA | |
| dc.date.accessioned | 2025-03-03T08:11:35Z | |
| dc.date.available | 2025-03-03T08:11:35Z | |
| dc.date.issued | 2025-01 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/16527 | |
| dc.description.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 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Electrical and Computer Engineering | en_US |
| dc.title | DEEP LEARNING BASED CRYPTOGRAPHY KEY GENERATION FROM BI-MODALBIOMETRIC FACE AND FINGERPRINT | en_US |
| dc.type | Thesis | en_US |
