Abstract
This chapter presents an overview of existing image datasets that can be used for evaluating approaches for overlapped fingerprint separation. It gives special attention to the Vlatacom dataset, created by the authors and publicly available, which consists of 120,000 synthetically overlapped test images (and the associated masks), with and without noise, processed with three different rotation angles, and in two variations of overall brightness.
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Notes
- 1.
If you are interested in obtaining the Vlatacom dataset, please send your request by email to branka.stojanovic@vlatacom.com. The archive includes all the image files (organized into folders as described in Sect. 2.3.3), the masks for all component images, the CSV files with x and y coordinates (and type) of singular points within the overlapped region, and a README file that explains how to use the dataset.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
Singular points are points where fingerprint ridges show discontinuity. Since overlapped fingerprint separation approaches usually rely, at least partially, on the continuity of ridges, they are more prone to errors when singular points appear in the overlapped area [11].
- 8.
500 ppi is a standard resolution for all latent fingerprint datasets.
- 9.
The MATLAB code for producing such variations is available upon request.
- 10.
Note that average brightness and presence or absence of noise have no impact on the masks.
- 11.
Please refer to the README file for more details about the naming convention used in the dataset, including the naming convention for the mask images.
- 12.
The transition from Tsinghua datasets to the proposed Vlatacom dataset mirrors, somehow, the creation of the Caltech 256 object category dataset (to replace its predecessor, Caltech 101) in the field of object recognition [4].
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Stojanović, B., Marques, O., Nešković, A. (2019). Latent Fingerprint Datasets. In: Segmentation and Separation of Overlapped Latent Fingerprints. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-23364-8_2
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