Biometric identification technology has become increasingly common in our daily lives as the requirement for information protection and control legislation has grown around the world. The unimodal biometric systems use only biometric traits to authenticate the user which is trustworthy but it possesses various limitations such as susceptibility to attacks, noise occurring in a dataset, non-universality challenges, etc. Multimodal biometrics technology has the potential to avoid the various fundamental constraints of unimodal biometric systems and also it has garnered interest and popularity in this respect. In this research, an efficient multimodal biometric recognition system based on a deep learning approach is proposed. The structure is implemented around convolutional neural networks (CNN) in which feature extraction and Softmax classifier are used to identify images. This method employs three CNN models for iris, face, and fingerprint were integrated to create the system. The two levels of fusion strategy such as feature level fusion and score level fusion were employed. The efficiency of the proposed model is evaluated based on the two most popular multimodal datasets as SDUMLA-HMT and BiosecureID biometric dataset. The result analysis demonstrates that the proposed multimodal biometric recognition provides the enhanced result with higher accuracy of 99.92%, a lower equal error rate of 0.10% on feature level, and 0.08% on score level fusion. Similarly, the average FAR is 0.09% and the average FRR is 0.06%. Because of this enhanced result, the proposed approach is computationally efficient.

随着世界各地对信息保护和控制立法的要求不断增加，生物特征识别技术在我们的日常生活中变得越来越普遍。单模态生物识别系统仅使用生物特征来验证值得信赖的用户，但它具有各种限制，例如易受攻击、数据集中出现噪声、非普遍性挑战等。多模态生物识别技术有可能避免各种基本问题单峰生物识别系统的限制，并且它在这方面也引起了人们的兴趣和欢迎。在这项研究中，提出了一种基于深度学习方法的高效多模态生物特征识别系统。该结构是围绕卷积神经网络 (CNN) 实现的，其中使用特征提取和 Softmax 分类器来识别图像。该方法采用虹膜、面部和指纹的三个 CNN 模型，它们被集成以创建系统。采用了特征级融合和分数级融合等两个级别的融合策略。基于两个最流行的多模式数据集 SDUMLA-HMT 和 BiosecureID 生物识别数据集评估了所提出模型的效率。结果分析表明，所提出的多模态生物特征识别提供了更高的准确率 99.92%、特征级别的等错误率为 0.10%、分数级别的融合为 0.08% 的增强结果。同样，平均 FAR 为 0.09%，平均 FRR 为 0.06%。由于这个增强的结果，