  With an exponential increase in number of users switching to mobile banking,
various countries are adopting biometric solutions as security measures. The
main reason for biometric technologies becoming more common in the everyday
lives of consumers is because of the facility to easily capture biometric data
in real time, using their mobile phones. Biometric technologies are providing
the potential security framework to make banking more convenient and secure
than it has ever been. At the same time, the exponential growth of enrollment
in the biometric system produces massive amount of high dimensionality data
that leads to degradation in the performance of the mobile banking systems.
Therefore, in order to overcome the performance issues arising due to this data
deluge, this paper aims to propose a distributed mobile biometric system based
on a high performance cluster Cloud. High availability, better time efficiency
and scalability are some of the added advantages of using the proposed system.
In this paper a Cloud based mobile biometric authentication framework
(BAMCloud) is proposed that uses dynamic signatures and performs
authentication. It includes the steps involving data capture using any handheld
mobile device, then storage, preprocessing and training the system in a
distributed manner over Cloud. For this purpose we have implemented it using
MapReduce on Hadoop platform and for training Levenberg-Marquardt
backpropagation neural network has been used. Moreover, the methodology adopted
is very novel as it achieves a speedup of 8.5x and a 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 lesser than the existing
ones. The experiments demonstrate that the better performance is achieved by
proposed framework as compared to the other methods used in the recent
literature.
