Existed fingerprint biometric systems.

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.

在数据安全领域，生物特征识别安全是日益受到关注的重要新兴研究议题。面向智能环境、具备更高识别精度与检测率的多模态生物特征识别系统，仍是极具挑战性的研究课题。将心电图（electrocardiogram, ECG）信号与指纹特征进行融合，是一种有效的多模态生物特征识别方案。本研究针对基于卷积神经网络（Convolutional Neural Network, CNN）的单模态与多模态生物特征识别系统开展实验，并与采用不同层级指纹与ECG信号融合策略的传统方法进行对比。本研究旨在评估所提出的并行、串行多模态生物特征识别系统在多种特征提取与分类方法下的有效性。此外，本研究还测试了基于深度学习与传统分类技术的ECG与指纹单模态生物特征识别系统的性能表现。本研究采用ECG（MIT-BIH）与指纹（FVC2004）公开数据集对所提出的生物特征识别系统进行性能评估。此外，本研究还开展了额外测试，分别在两类虚拟数据集上验证所提模型的性能：1）未进行数据增强的虚拟数据集（ODB）；2）经过数据增强的虚拟数据集（VDB）。研究结果显示，并行多模态系统的最优性能可达0.96的受试者工作特征曲线下面积（Area Under the ROC Curve, AUC），串行多模态系统则可达0.99的AUC值；而指纹与ECG单模态生物特征识别系统的AUC值分别为0.87与0.99。所提出的多模态生物特征识别系统的整体性能，优于基于CNN的单模态生物特征识别系统。此外，针对ECG信号的CNN模型与基于神经网络的串行多模态系统，其性能表现同样优于其余各类系统。最后，本研究将所提系统的性能表现与已有相关研究成果进行了对比。