FINGERPRINT RECOGNITION IN LOW-QUALITY IMAGES

Algorithms are identified which are best suited for an automatic fingerprint recognition system operating on low quality images. New preprocessing algorithms for noise removal and binarization are described. Three approaches to classification are investigated: a correlation classifier, and two feature-based classification schemes. The best results on a database of 80 fingerprints are obtained with spatial-frequency features. Three classifiers (neural net, linear classifier and nearest neighbour) using these features are successful in identifying an independent test set. Details of the results are shown. In conclusion suggestions are made concerning the most suitable algorithms in each of the processing steps.