基于深度学习的掌纹和手背静脉多模态生物特征融合

Norah Abdullah Al-johani, Lamiaa A. Elrefaei
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引用次数: 3

摘要

生物识别技术的进步已经取得了相对较高的识别率。然而,对可靠、健壮和方便的生物识别系统的需求仍然存在。使用掌纹(PP)进行验证的系统具有许多优点,包括稳定的线条特征,减少失真和简单的自定位。手背静脉(dhv)对每个人来说都是不同的,即使是同卵双胞胎也有不同的dhv。dhv似乎随着时间的推移保持稳定。过去,不同的特征算法被用来实现掌纹(PP)和手背静脉(DHV)系统。以前的系统依赖于手工制作的算法。深度学习(DL)在卷积神经网络(CNN)学习特征方面的进步,使其在PP和DHV识别系统中得到了应用。本文采用(VGG16, VGG19和AlexNet) CNN模型,提出了一种基于PP和DHV的多模态生物识别系统。该系统采用两种方法:特征级融合(FLF)和分数级融合(SLF)。在第一种方法中,使用CNN模型提取PP和DHV的特征。然后使用串行或并行融合将这些提取的特征融合,并使用支持向量机(SVM)训练纠错输出代码(ECOC)进行分类。在第二种方法中,通过应用两种策略,使用sum, max和product方法完成分数级别的融合:使用CNN模型对PP和DHV进行特征提取和分类的迁移学习,然后进行分数级别融合。第二种策略是利用CNN模型对PP和DHV进行特征提取,并利用SVM训练ECOC进行分类,然后进行评分水平融合。使用两个DHV数据库和一个PP数据库对系统进行了测试。通过对每个DHV数据库重复PP数据库,对多模态系统进行了两次测试。该系统达到了很高的准确率。
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Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning
Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Previous systems relied on handcrafted algorithms. The advancements of deep learning (DL) in the features learned by the convolutional neural network (CNN) has led to its application in PP and DHV recognition systems. In this article, a multimodal biometric system based on PP and DHV using (VGG16, VGG19 and AlexNet) CNN models is proposed. The proposed system is uses two approaches: feature level fusion (FLF) and Score level fusion (SLF). In the first approach, the features from PP and DHV are extracted with CNN models. These extracted features are then fused using serial or parallel fusion and used to train error-correcting output codes (ECOC) with a support vector machine (SVM) for classification. In the second approach, the fusion at score level is done with sum, max, and product methods by applying two strategies: Transfer learning that uses CNN models for features extraction and classification for PP and DHV, then score level fusion. For the second strategy, features are extracted with CNN models for PP and DHV and used to train ECOC with SVM for classification, then score level fusion. The system was tested using two DHV databases and one PP database. The multimodal system is tested two times by repeating PP database for each DHV database. The system achieved very high accuracy rate.
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