When the database used for this process is big, a problem may arise when implementing for applications in real-time due to a large number of comparisons required increasing the computation power and time required.
Nowadays there are many fingerprint based systems that authenticate at high speeds that is because of the detailed clarity of the input provided to these algorithm. When there is a sample data provided to them with less detailed features it mostly leads to misclassification. We heavily rely on powerful sensors to provide us with great input. However, in this paper we would be discussing how we can achieve similar accuracy levels by utilizing secure verification methods to similarly classify input with substantial noise. This research will show the performance of using enhanced verification platforms along side deep learning structures over traditional systems.
We will also detail the methodology, description of the database used in this work, architecture of the adopted networks, parameters for training, and the evaluation protocol. Finally, the results will be presented and discussed, and the conclusions drawn.
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