Images selected by using template selection methods constitute the template database and the remaining images of the finger constitute the query database.
In this case, the three templates selected from FVC2004DB4 are all chosen by using the template selection method.
In template selection, the memory overhead was around 6.1 MB when 20% templates were selected to be the representatives.
See Kulkarni, et. al. [references] for further details of the issues surrounding template selection.
This facile processing route provides for excellent size control of the HCNS product via appropriate AgNP template selection.
In addition, a refined template selection method was proposed to reduce the computation complexity and to improve the recognition ability.
Based on this, we introduce the correlation coefficient matrix to determine an optimal template, and the accuracy of template selection algorithm is also discussed.
The proposed template selection algorithm takes advantage of the FPGA area-time measures of the enumerated patterns, which can be easily inferred from the FPGA-aware enumeration strategy.
A novel template selection strategy was employed to reduce the number of templates required for matching by up to 50%, while providing comparable performance with known approaches.
In this section, the proposed template selection is compared to MDIST [10] template selection.
In order to reduce computation and storage complexities, we also propose two methods for template selection: minimum distance template selection (MDTS) and maximum likelihood template selection (MLTS).
