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Images selected by using template selection methods constitute the template database and the remaining images of the finger constitute the query database.
The template selection methods also provided significant accuracy gains over the HMM baseline while largely reducing the computation and storage complexities.
To further reduce the costs of memory space and computation, we propose template selection methods to generate template representatives based on the criteria of minimum distance (MDTS) and maximum likelihood (MLTS) and we also propose a template compression method to integrate information from training templates to obtain more informative template representatives.
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In the first stage, enrollment stage, we adopt an effective template selection method to select those fingerprints which best represent a finger, and then, a polyhedron is created by the matching results of multiple template fingerprints and a virtual centroid of the polyhedron is given.
In this case, the three templates selected from FVC2004DB4 are all chosen by using the template selection method.
In Table 2, we show how the threshold value t of Equation 19 for removing the GMM outliers affected the recognition performance, where the template selection method was MLTS with the KL distance, and 20% template representatives were selected.
Our proposed template selection method is better than MDIST.
The template selection method is described as follows.
In addition, a refined template selection method was proposed to reduce the computation complexity and to improve the recognition ability.
Future study is to be done on more samples to further verify the performance of both the new template selection method and the proposed framework.
In Table 4, we use minutiae-based method as the base matcher, compared to Table 2, for the same templates, our proposed framework has a more performance than only using template selection method.
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