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 finger print 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 Neightbour (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, ( C-6(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.