With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

中文翻译：

---

虹膜，面部和手指静脉特征融合的多模式生物特征识别系统的深度学习方法

随着全世界对信息安全和安全法规的需求不断增长，生物识别技术已广泛应用于我们的日常生活中。在这方面，多峰生物特征技术由于其克服了单峰生物特征系统的许多显着局限的能力而引起了人们的兴趣并开始流行。本文提出了一种新的多模式生物特征识别系统，该系统基于深度学习算法，利用虹膜，面部和手指静脉的生物特征识别人类。系统的结构基于卷积神经网络（CNN），可通过softmax分类器提取特征并对图像进行分类。为了开发该系统，将三个CNN模型组合在一起。一种用于虹膜，一种用于面部，另一种用于手指静脉。为了建立CNN模型，使用著名的相关模型VGG-16，应用Adam优化方法，并使用分类交叉熵作为损失函数。应用了一些避免过度拟合的技术，例如图像增强和辍学技术。为了融合CNN模型，采用了不同的融合方法来探讨融合方法对识别性能的影响，因此，采用了特征和得分级别的融合方法。通过对SDUMLA-HMT数据集（这是一个多模式生物特征数据集）进行几次实验，对所提出系统的性能进行了经验评估。获得的结果表明，在生物识别系统中使用三个生物特征比使用两个或一个生物特征获得更好的结果。