Active development year: 2012
Some implementations of fingerprint recognition algorithms developed for Biometric Methods course at University of Wrocław, Poland.
Simply do python filename.py --help
to figure out how to execute filename
algorithm
Finds singular points on fingerprint.
How it works (more detailed description here):
block_size
The python script will mark the singularities with circles:
Example: python poincare.py images/ppf1.png 16 1 --smooth
Images:
Note: algorithm marked singular points not only inside fingerprint itself, but on its edges and even outside. This is a result of usage of non-preprocessed image - if the image was enhanced (better contrast, background removed), then only singular points inside fingerprint would be marked.
How it [works] (http://bme.med.upatras.gr/improc/Morphological%20operators.htm#Thining)
Example: python thining.py images/ppf1_enhanced.gif --save
Images:
Crossing number methods is a really simple way to detect ridge endings and ridge bifurcations.
First, you'll need thinned (skeleton) image (refer to previous section how to get it). Then the crossing number algorithm will look at 3x3 pixel blocks:
Example: python crossing_number.py images/ppf1_enhanced_thinned.gif --save