Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

• Published in: PloS one Vol. 19; no. 2; p. e0291084
• Main Authors: A El Rahman, Sahar, Alluhaidan, Ala Saleh
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.02.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Biometric Identification - methods; Biometry; Biometry - methods; Computer and Information Sciences; Deep Learning; Electrocardiogram; Electrocardiography; Electrocardiography - methods; Engineering and Technology; Machine learning; Medicine and Health Sciences; Methods; Neural networks; Neural Networks, Computer; Research and Analysis Methods; Safety and security measures; Social Sciences; Taiwan; Saudi Arabia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0291084

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.