A Robust Biometric Authentication Framework for Access Control

Abstract

Unauthorized access poses significant security concerns, jeopardizing the confidentiality, integrity, and
availability of critical data and resources. Ensuring authorized access is essential for protecting sensitive systems
across diverse fields, including smart buildings, military bases, hospitals, airports, and financial institutions.
Biometric authentication has emerged as a reliable solution for access control, leveraging unique human traits for
verification. However, traditional feature-based biometric systems are limited by environmental sensitivity, poor
generalization, and vulnerability to spoofing, while deep learning-based systems face challenges such as high
computational demands, reliance on large datasets, and lack of interpretability. To address these limitations, this
research proposes a hybrid biometric authentication framework that combines the strengths of deep learning,
specifically Residual Network (ResNet)-a Convolutional Neural Network (CNN), with the Local Binary Pattern
(LBP) method. By integrating interpretable, computationally efficient features from LBP with ResNet’s ability to
learn complex patterns, the framework improves robustness, reduces overfitting, and enhances scalability. This
approach offers a balanced, efficient solution for secure biometric authentication, tailored for real-world and
resource-constrained environments.