Abstract
There has been an exponential increase in the number of users switching to mobile banking. Therefore, various countries are adopting biometric solutions as security measures. Biometric technologies provide the potential security framework to make banking more convenient and secure than it has ever been. These technologies are gaining much popularity because of the ease in capturing biometric data in real-time using one's mobile phone. At the same time, the exponential growth of enrollment in the biometric system produces a massive amount of high-dimensional data. To overcome performance-related issues arising due to the resulting data deluge, this paper aims to propose a distributed mobile biometric system based on a high-performance cluster Cloud. In this paper, a Cloud-based mobile biometric authentication framework (BAMCloud) is proposed that uses dynamic signatures for authentication. The process flow of the BAMCloud system involves capturing data using any handheld mobile device, followed by its storage, preprocessing, and training of the system in a distributed manner over the Cloud. MapReduce has been implemented on the Hadoop platform to reduce the processing time. For model training, The Levenberg-Marquardt backpropagation neural network has been used. It achieves a speed of 8.5 times the original speed and performance of 96.23%. Furthermore, the cost-benefit analysis of the implemented system shows that the cost of implementation and execution of the system is less than the existing ones. The experiments demonstrate that better performance is achieved by implementing the proposed framework as compared to other methods used in recent literature.