Abstract
In this paper, we propose an automatic approach to measure the minutiae quality. When image of 500 dpi is captured, immediately the enhancement, thinning and minutiae extraction processes are executed. The basic idea is to detect the spatial β 0 – Connected minutiae cluster using the Euclidean distance and quantify the number of element for each group. In general, we observe that more than five element in a group is a clue to mark all points in the cluster as bad minutiae. We divide the image in block of 20 x 20 pixels. If one block contains bad minutiae it is mark as a bad block. The goodness quality index is calculated as the proportion of bad blocks respect to the number of total blocks. The proposed index was tested on the FVC2000 fingerprint image database.
Keywords
- Fingerprint Image
- Graph Base Representation
- Middle Step
- High Spatial Density
- Automatic Fingerprint Identification System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Reyes, E.G., Rodríguez, J.L.G., Ham, M.I. (2005). An Automatic Goodness Index to Measure Fingerprint Minutiae Quality. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_60
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DOI: https://doi.org/10.1007/11578079_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)
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