%0 Journal Article %1 Tan:2006:tSMC %A Tan, Xuejun %A Bhanu, B. %A Lin, Yingqiang %D Aug %J IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews %K (artificial Bayes Bayesian NIST-4 algorithm, algorithms, classification classification, classifier, composite database, databases discovery, extraction, feature feature-learning fingerprint genetic identification, image intelligence), learning method, methods, operations operator primitive processing programming, visual %N 3 %P 287--300 %R 10.1109/TSMCC.2005.848167 %T Fingerprint classification based on learned features %V 35 %X In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3percent and 91.6percent, respectively, which compare favourably with other published research and are one of the best results published to date.

@article{Tan:2006:tSMC, abstract = {In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3percent and 91.6percent, respectively, which compare favourably with other published research and are one of the best results published to date.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Tan, Xuejun and Bhanu, B. and Lin, Yingqiang}, biburl = {https://www.bibsonomy.org/bibtex/2797af734fa2ebc20d8ad574c5a7eeddd/brazovayeye}, doi = {10.1109/TSMCC.2005.848167}, interhash = {fccb4e2cdd23d80d8db7162a74e835a4}, intrahash = {797af734fa2ebc20d8ad574c5a7eeddd}, issn = {1094-6977}, journal = {IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews}, keywords = {(artificial Bayes Bayesian NIST-4 algorithm, algorithms, classification classification, classifier, composite database, databases discovery, extraction, feature feature-learning fingerprint genetic identification, image intelligence), learning method, methods, operations operator primitive processing programming, visual}, number = 3, pages = {287--300}, timestamp = {2008-06-19T17:52:35.000+0200}, title = {Fingerprint classification based on learned features}, volume = 35, year = {Aug} }