Development of a novel algorithm for SVMBDT fingerprint classifier based on clustering approach

A novel method for fingerprint recognition, using SVM has been proposed in this paper wherein large sample size problem is reduced to small sample size problem using support vectors. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. This paper presents an effective method for fingerprint classification using data mining approach. Initially, it generates a numeric code sequence for each fingerprint image based on the ridge flow patterns. Then for each class, a seed is selected by using a frequent itemsets generation technique. These seeds are subsequently used for clustering the fingerprint images. The proposed method was tested and evaluated in terms of several real-life datasets and a significant improvement in reducing the misclassification errors has been noticed in comparison to its other counterparts.