Fingerprint classification based on genetic programming
Jiaojiao Hu, Mei Hua Xie · 2010
In this paper, we present a novel algorithm for fingerprint classification. This algorithm classifies a fingerprint image into one of the five classes: Arch, Left loop, Right loop, Whorl, and Tented arch. Initially, preprocessing of fingerprint images is carried out to enhance the image. Then we use genetic programming (GP) to generate new features from the original dataset without prior knowledge. Finally we can classify the fingerprint through a combination of BP network and SVM classifiers, which can not only supplement their advantages, but also improve the computation efficiency. We experiment this algorithm on database from FVC2004. For the five-class problem, a classification accuracy of 93.6% without any reject, and classification accuracy of 96.2% with a 15% reject rate. For the four-class problem (arch and tented arch combined into one class), classification error can be reduced to 3.6% with only 7.2% reject rate.