Multimodal biometrics is an emerging technology for distributed data security. Single and multi-user data authentication plays a vital role in commercial or e-governance applications. Many approaches have been implemented in literature to secure the single user data using biometric security systems. Most of these systems are based on static initialization parameters and fixed multi-modal biometric features for data authentication. Also, traditional multi-modal biometric based data authentication schemes are independent of dynamic variation in integrity verification. In order to overcome these problems, a new multi-user based multi-modal authentication framework is designed and implemented on large image data types. In this framework, different biometric features such as IRIS, facial and fingerprint features are used to find the unique integrity of user for data authentication and security process. A new integrity computational algorithm and encryption technique are implemented to provide the strong data integrity verification and data security in distributed applications. Experimental results show that the proposed multi-modal integrity-based encryption model has nearly 7% of computational integrity bit change and 5% of runtime on large dataset.

中文翻译：

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适用于分布式应用程序的高效多模态生物特征传感和身份验证框架

多模态生物识别技术是一种新兴的分布式数据安全技术。单用户和多用户数据认证在商业或电子政务应用中起着至关重要的作用。文献中已经实施了许多方法来使用生物特征安全系统来保护单用户数据。这些系统中的大多数基于静态初始化参数和固定的多模态生物特征进行数据认证。此外，传统的基于多模态生物特征的数据认证方案独立于完整性验证的动态变化。为了克服这些问题，在大图像数据类型上设计并实现了一种新的基于多用户的多模态认证框架。在这个框架中，不同的生物特征如 IRIS、面部和指纹特征用于发现用户的唯一完整性以进行数据认证和安全过程。实现了一种新的完整性计算算法和加密技术，为分布式应用提供强大的数据完整性验证和数据安全性。实验结果表明，所提出的基于多模态完整性的加密模型在大型数据集上具有接近 7% 的计算完整性位变化和 5% 的运行时间。