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Convolutional Neural Network Approach Based on Multimodal Biometric System with Fusion of Face and Finger Vein
Yang Wang1, Dekai Shi1, Weibin Zhou1
1School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300453, China.
Sensors (Basel, Switzerland)
|August 26, 2022
Summary
This study introduces a multimodal biometric system combining finger vein and face recognition for enhanced security. The novel approach achieves over 98.4% accuracy, improving identity verification in complex scenarios.
Area of Science:
- Computer Science
- Biometrics
- Artificial Intelligence
Background:
- Accurate identity verification is crucial in the digital age.
- Single biometric systems face limitations in diverse authentication scenarios.
- Multimodal biometrics offer enhanced accuracy and security.
Purpose of the Study:
- To propose a novel biometric method for improved identity identification.
- To enhance security and accuracy using multimodal biometric fusion.
- To investigate the effectiveness of feature-layer fusion for biometrics.
Main Methods:
- Developed a bimodal biometric fusion method using finger vein and face data.
- Employed a convolutional neural network (CNN) for feature extraction and fusion.
- Integrated self-attention mechanisms and RESNET residual structures for feature weighting and concatenation.
Main Results:
- Experimental validation using AlexNet and VGG-19 models.
- Achieved recognition accuracy exceeding 98.4% for the proposed bimodal fusion method.
- Demonstrated the high efficiency and superior performance of feature-layer fusion.
Conclusions:
- The proposed finger vein and face bimodal feature layer fusion is highly effective.
- Multimodal biometric systems significantly improve identity verification accuracy and security.
- This approach addresses the limitations of single biometric modalities.
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