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首页> 外文期刊>International Journal of Computational Science and Engineering >Transforming grid to cloud services for multimodal biometrics
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Transforming grid to cloud services for multimodal biometrics

机译：将网格转换为云服务以实现多模式生物识别

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Various barriers due to performance, security and privacy issues associated with shared heterogeneous data centres impede the diffusion of multimodal biometrics that could enhance secured systems in our everyday lives. This paper identifies the issues associated with multimodal fusion and privacy risks and provides means of addressing them. It presents the need for transforming from grid-based services to cloud-based services in order to address these issues, paving the way towards a successful user acceptance of biometric systems. Though grid services could provide heterogeneous resource sharing capabilities and security, multimodal biometrics require scalability and pricing flexibility for gaining user-acceptance and easy adoption. By leveraging 'on-the-demand' services offered in cloud computing, reliable and context-sensitive privacy policies could be applied for multimodal biometrics to cater to the different business and user requirements. Further, the paper combines the concept of cloud services along with momentum back propagation induced artificial neural network (MBP-ANN) model of machine learning for efficient multimodal fusion and flexible authentication schemes. The paper also describes a case scenario of how this model could be adopted by applying the varying security requirements and user privacy preferences for a successful diffusion of multimodal biometrics in the cloud.
机译：与共享异构数据中心相关的性能，安全性和隐私问题导致各种障碍，阻碍了多模态生物识别技术的普及，这可以增强我们日常生活中的安全系统。本文确定了与多模式融合和隐私风险相关的问题，并提供了解决这些问题的方法。为了解决这些问题，提出了从基于网格的服务转换为基于云的服务的需求，为成功地接受生物特征识别系统的用户铺平了道路。尽管网格服务可以提供异构的资源共享功能和安全性，但是多模式生物识别技术需要可伸缩性和定价灵活性才能获得用户的认可并易于采用。通过利用云计算中提供的“按需”服务，可以将可靠且上下文相关的隐私策略应用于多模式生物识别，以满足不同的业务和用户需求。此外，本文将云服务的概念与动量反向传播诱导的人工神经网络（MBP-ANN）机器学习模型相结合，以实现高效的多模式融合和灵活的身份验证方案。本文还描述了一个案例场景，该案例场景说明了如何通过应用不断变化的安全要求和用户隐私首选项，成功地在云中传播多模式生物识别技术。

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