article · PLoS ONE
Biometric security systems face challenges in achieving high accuracy and detection rates within smart environments. Combining different biological markers, such as electrocardiogram (ECG) heart signals and fingerprints, offers a pathway to more dependable recognition systems. Evaluations were carried out comparing single-trait unimodal systems with parallel and sequential multimodal fusion architectures. These systems combined traditional classification techniques and deep learning methods based on convolutional neural networks. Testing was conducted on standard reference databases, specifically MIT-BIH for ECG and FVC2004 for fingerprints, alongside virtual datasets tested with and without data augmentation. The sequential multimodal system demonstrated the highest overall accuracy, attaining an Area Under the ROC Curve score of 0.99, while the parallel system reached 0.96. Deep learning models applied to ECG signals and sequential multimodal integration consistently outperformed standalone fingerprint recognition and traditional classifiers.
Single-factor biometric security, like fingerprint scanning, can be vulnerable to spoofing or degraded performance. Integrating internal physiological signals like heart rhythms with physical prints makes identity verification significantly more robust. This multi-layered approach helps secure data and connected devices in smart environments, offering higher confidence that an individual is genuinely who they claim to be.
This approach could enhance access control and user authentication systems for smart environments requiring high data security. Potential users include security technology developers and facilities managers seeking spoof-resistant identity verification. The technology appears to be applied research tested on benchmark and virtual laboratory datasets, meaning further hardware integration and live testing in operational settings are necessary before commercial implementation.
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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.
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DOI: 10.1371/journal.pone.0291084
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