Exact(2)
Experimental results on two publicly available latent fingerprint datasets show that the proposed algorithm yields state-of-the-art results for automated minutia extraction.
Experiments are conducted with seven real fingerprint datasets (respectively, FPI-57, FPI-78,..., and FPI-389, with 57, 78,..., and 389 colleagues; between 15 and 20 fingerprints were captured for each individual).
Similar(58)
x i is the fingerprint dataset entry i, i.e., row i of RSS.
In the experiments, we use a real-world WiFi fingerprint dataset to evaluate the performance of our algorithm.
Furthermore, the tolerances of 1 - 7 were tested using the entire fingerprint dataset to determine the parameters suitable for contig assembly.
The final AFLP fingerprint dataset for physical map construction was produced after a number of processing and cleaning steps, involving preliminary versions of the physical map.
In addition, tolerances of 4 - 10 were tested using the entire fingerprint dataset to determine the parameters suitable for contig assembly.
Cutoff values (probability threshold that fingerprint bands match by coincidence) of 1e-20 - 1e-02 were tested using the entire fingerprint dataset, and the resultant numbers of contigs, singletons, and questionable-clones (Q-clones) were analyzed.
To determine the tolerance value to be used for the contig assembly, we selected the four pECBAC1 vector fragments generated with the enzyme combination (Hind III/ Xba I/ Xho I/ Hae III) of sizes 60, 161, 230 and 375 bases in the range from 35 to 500 bases released from 200 BACs randomly selected from the fingerprint dataset.
Although matched and non-matched records differ in terms of some of their background characteristics, the distribution of background characteristics in the fingerprint linked dataset and the dataset generated via record linkage on conventional personal identifiers is quite similar for all the three scenarios considered here (Table 4).
These findings increased confidence in the outputs from the local optimisation mass balance modelling, but fingerprint property datasets should be treated on an individual basis.
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