A Hybrid Deep Learning Framework for Multimodal Biometric Verification Utilizing ECG, Fingerprint, and Iris Data

Single-trait biometric systems frequently face criti- cal security flaws, ranging from physical sensor wear to sophisti- cated spoofing attacks. When deploying modern deep learning to solve these issues, developers encounter a new problem: data starvation. Standard classification models require thou- sands of images to generalize, leading to massive overfitting when only a few enrollment scans are available. To overcome this limitation, we engineered a robust multimodal biometric architecture tailored specifically for data-scarce constraints (a maximum of three training samples per user). Our solution fuses a dynamic, one-dimensional behavioral signal (Electrocar- diogram) with static, two-dimensional physical traits (Finger- print and Iris) across a dataset of 199 unique individuals. To bypass the failure of standard Convolutional Neural Networks (CNNs) on sparse physical data, we shifted the network objective from 1-to-N identification to 1-to-1 verification using Siamese architectures. We processed the physical traits through parallel Siamese ResNet50V2 networks optimized with contrastive loss to generate 512-dimensional feature embeddings. Simultaneously, a custom-built 1D-CNN evaluated the temporal ECG data. We then normalized and merged the outputs from these varying modalities using a targeted, weighted score-level fusion algorithm. Testing showed that while traditional 2D Softmax classifiers stalled below 40% accuracy, our Siamese metric learning approach drastically elevated unimodal performance. The combined multimodal sys- tem (anchored by a 50% ECG, 30% Iris, and 20% Fingerprint weight distribution) achieved a final accuracy of 92.21%. Further- more, the system recorded a False Acceptance Rate (FAR) of just 1.01% alongside a 14.57% False Rejection Rate (FRR), proving that our hybrid fusion methodology mathematically neutralizes the weaknesses of individual biometrics in highly constrained environments.