Transforming grid to cloud services for multimodal biometrics
by Sitalakshmi Venkatraman
International Journal of Computational Science and Engineering (IJCSE), Vol. 13, No. 1, 2016

Abstract: 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.

Online publication date: Thu, 14-Jul-2016

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