Hyperparameter Optimization in Deep Learning Techniques for Multimodal Biometric Verification

Abstract

Biometrics plays a crucial role in mitigating threats such as theft, duplication, and cracking by offering more secure verification methods. To enhance system reliability, researchers are increasingly focusing on multimodal biometrics that integrate facial recognition and fingerprint identification. The objective is to design a biometric verification system that leverages deep learning to automatically extract and analyze features from fingerprints, videos, and facial images. This system employs image scaling and data augmentation during preprocessing to preserve information and reduce computational time. To strengthen resistance against software attacks and varying poses, dynamic fusion techniques applied to hand-surface features are incorporated. Furthermore, multi-scale single-shot face detectors enable efficient face detection in unconstrained videos, while memory-efficient deep neural networks (DNNs) ensure optimal resource utilization. The study applies advanced approaches such as Transfer Learning and Hyperparameter Optimization algorithms, including Keras Tuner (Random Search), Genetic-CNN, Teaching Learning Based Optimization (TLBO), and Grey Wolf Optimizer (GWO). Findings demonstrate that models integrated with hyperparameter optimization significantly outperform those without optimization. For facial recognition, CNN-GA achieved an impressive classification accuracy of 99.75%, while in fingerprint recognition, Keras Tuner recorded a peak accuracy of 99.09%. These outcomes highlight the effectiveness of combining deep learning with optimization strategies in building robust multimodal biometric systems. By integrating efficient preprocessing, adaptive algorithms, and optimized architectures, the proposed framework not only enhances accuracy but also ensures resilience against diverse attack vectors, positioning multimodal biometrics as a key solution for future secure authentication technologies.