Alshardan Amal, Kumar Arun, Alghamdi Mohammed, Maashi Mashael, Alahmari Saad, Alharbi Abeer A K, Almukadi Wafa, Alzahrani Yazeed
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Riyadh, Saudi Arabia.
Department of Computer Science & Engineering, G.L Bajaj Institute of Technology and Management, Gr. Noida, India.
PeerJ Comput Sci. 2024 Oct 31;10:e2440. doi: 10.7717/peerj-cs.2440. eCollection 2024.
Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the "NUPT-FPV" dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.
多模态生物识别技术的进步整合了多种生物特征，有望提高识别系统的准确性和鲁棒性。本研究聚焦于通过使用指纹和指静脉图像作为主要特征来改进多模态生物识别。我们使用了“NUPT-FPV”数据集，该数据集包含大量指静脉和指纹图像，这对我们的研究有很大帮助。卷积神经网络（CNN）在计算机视觉任务中效率显著，我们在模型中使用它来提取独特的判别特征。具体来说，我们纳入了三种流行的CNN架构：ResNet、VGGNet和DenseNet。我们探索了安全应用中使用的三种融合策略：早期融合、晚期融合和分数级融合。早期融合在单个CNN的输入层整合原始图像，在初始阶段合并信息。相比之下，晚期融合在每个CNN模型单独学习后合并特征。分数级融合采用加权聚合来组合来自每个模态的分数，利用它们提供的互补信息。我们还使用对比度受限自适应直方图均衡化（CLAHE）来增强指纹对比度和静脉模式特征，提高特征的可见性和提取效果。我们的评估指标包括准确率、等错误率（EER）和ROC曲线。CNN架构与增强方法的融合在多模态生物识别中表现出了良好的性能，旨在提高识别准确率。所提出的模型提供了一个使用多种生物特征来验证身份的可靠认证系统。
