Gabor filter based fingerprint classification using support vector machines

Fingerprint classification is important for different practical applications. An accurate and consistent classification can greatly reduce fingerprint-matching time for large databases. We use a Gabor filter-based feature extraction scheme to generate a 384 dimensional feature vector for each fingerprint image. The classification of these patterns is done through a novel two stage classifier in which K nearest neighbour (KNN) acts as the first step and finds out the two most frequently represented classes amongst the K nearest patterns, followed by the pertinent SVM classifier choosing the most apt class of the two. 6 SVMs have to be trained for a four class problem, (/sup 6/C/sub 2/), that is, all one-against-one SVMs. Using this novel scheme and working on the FVC 2000 database (257 final images) we achieved a maximum accuracy of 98.81% with a rejection percentage of 1.95%.This is significantly higher than most reported results in contemporary literature. The SVM training time was 145 seconds, i.e., 24 seconds per SVM on a Pentium III machine.