An Automatic Goodness Index to Measure Fingerprint Minutiae Quality

  • Edel García Reyes
  • José Luis Gil Rodríguez
  • Mabel Iglesias Ham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


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.


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Edel García Reyes
    • 1
  • José Luis Gil Rodríguez
    • 1
  • Mabel Iglesias Ham
    • 1
  1. 1.Advanced Technologies Application CenterPlaya, Ciudad de La HabanaCuba

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