Multimodal Biometric Authentication using Convolutional Neural Network, Swin Transformer, and Multi-Head Attention with Global Max Pooling

Multimodal biometric authentication is a robust security mechanism designed to enhance the reliability and security of user-authentication systems. It integrates several biometric traits to provide an accurate and secure identification system. However, existing Deep Learning (DL) models struggle to capture both spatial and contextual dependencies across multiple biometric traits, which reduces their robustness against spoofing attacks. Hence, this paper proposes a convolutional neural network, swin transformer, multi-head self-attention, and global max pooling (CSMG) for effective feature extraction, strengthening both spatial and contextual representation, and reducing redundancy across modalities. Next, the classification head uses the extracted feature map and softmax layer to predict a person’s identity. An effective fusion strategy was introduced to integrate fingerprint, iris, and ECG signals, utilizing their complementary strengths to mitigate spoofing attacks. The performance of the proposed CSMG method was evaluated using the IITD Iris, SOCOfing fingerprint, and HEARTPRINT ECG datasets. The experimental evaluation demonstrates that the proposed CSMG method achieves a recognition accuracy of 99.90% for fingerprints, 99% for irises, and 99% for ECG compared to traditional models.