Multimodal Biometric Recognition Based on Fusion of Electrocardiogram and Fingerprint Using CNN, LSTM, CNN-LSTM, and DNN Models

Biometric authentication offers a promising solution for enhancing the security of digital systems by leveraging individuals' unique physiological characteristics. This study proposes a multimodal authentication system using deep learning approaches to integrate fingerprint images and electrocardiogram (ECG) signals. The datasets employed include FVC2004 for fingerprint data and ECG-ID for ECG signals. Four deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Deep Neural Network (DNN)—are evaluated to compare their effectiveness in recognizing individual identity based on fused multimodal features. Feature extraction techniques include grayscale conversion, binarization, edge detection, minutiae extraction for fingerprint images, and R-peak–based segmentation for ECG signals. The extracted features are combined using a feature-level fusion strategy to form a unified representation. Experimental results indicate that the CNN model achieves the highest classification accuracy at 96.25%, followed by LSTM and DNN at 93.75%, while CNN-LSTM performs the lowest at 11.25%. Minutiae-based features consistently yield superior results across different models, highlighting the importance of local feature descriptors in fingerprint-based identification tasks. This research advances biometric authentication by demonstrating the effectiveness of feature-level fusion and CNN architecture for accurate and robust identity recognition. The proposed system shows strong potential for secure and adaptive biometric authentication in modern digital applications.