Risk-Based Neuro-Grid Architecture for Multimodal Biometrics

Abstract

Recent research indicates that multimodal biometrics is the way forward for a highly reliable adoption of biometric identification systems in various applications, such as banks, businesses, government and even home environments. However, such systems would require large distributed datasets with multiple computational realms spanning organisational boundaries and individual privacies.

In this paper, we propose a novel approach and architecture for multimodal biometrics that leverages the emerging grid information services and harnesses the capabilities of neural network as well. We describe how such a neuro-grid architecture is modelled with the prime objective of overcoming the barriers of biometric risks and privacy issues through flexible and adaptable multimodal biometric fusion schemes. On one hand, the model uses grid services to promote and simplify the shared and distributed resource management of multimodal biometrics, and on the other hand, it adopts a feed-forward neural network to provide reliability and risk-based flexibility in feature extraction and multimodal fusion, that are warranted for different real-life applications. With individual autonomy, scalability, risk-based deployment and interoperability serving the backbone of the neuro-grid information service, our novel architecture would deliver seamless and robust access to geographically distributed biometric data centres that cater to the current and future diverse multimodal requirements of various day-to-day biometric transactions.