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

Sahar A El Rahman¹, Ala Saleh Alluhaidan²

¹Department of Electrical Engineering, Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt.

|February 15, 2024

Summary

Multimodal biometrics combining electrocardiogram (ECG) and fingerprint data significantly enhance security. These fused systems, particularly sequential fusion using Convolutional Neural Networks (CNN), show superior performance over unimodal methods for smart environments.

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Area of Science:

Biometric Security
Data Security
Pattern Recognition

Background:

Biometric security is crucial for data security, with multimodal systems offering enhanced accuracy.
Integrating electrocardiogram (ECG) signals and fingerprint data presents an effective multimodal approach.
Challenges remain in developing accurate and reliable multimodal biometrics for smart environments.

Purpose of the Study:

To evaluate unimodal and multimodal biometric systems using Convolutional Neural Networks (CNN).
To compare traditional methods with deep learning approaches for fingerprint and ECG fusion.
To assess the effectiveness of parallel and sequential fusion strategies in multimodal biometrics.

Main Methods:

Developed and compared unimodal and multimodal biometric systems using CNN.
Employed various feature extraction and classification techniques for fingerprint and ECG fusion.
Evaluated systems on MIT-BIH (ECG) and FVC2004 (fingerprint) databases, including virtual datasets with and without augmentation.

Main Results:

Sequential multimodal biometrics achieved an optimal Area Under the ROC Curve (AUC) of 0.99.
Parallel multimodal biometrics achieved an AUC of 0.96, outperforming unimodal fingerprint biometrics (0.87 AUC).
The proposed CNN models, especially for ECG and sequential fusion, demonstrated superior performance compared to other systems.

Conclusions:

Multimodal biometrics, particularly using CNN, significantly outperform unimodal systems in accuracy and detection rates.
Sequential fusion of ECG and fingerprint data shows exceptional promise for robust biometric security.
The proposed CNN-based approach offers a powerful solution for advanced biometric authentication in smart environments.