A Programming Based Boosting in Super-Classifier for Fingerprint Recognition
Sumana Kundu, Goutam Sarker · Advances in intelligent systems and computing · 2016
A super-classifier with programming based boosting has been designed and established for fingerprint recognition. This multiple classifier set is comprised of three different classifiers. The first classifier is an OCA based modified RBFN with BP learning, second classifier is a combination of Malsburg learning and BP Network and third classifier is a SOM based modified RBFN with BP learning. These three individual classifiers perform fingerprint identification separately and these are fused together in a super-classifier which integrates the different conclusions using programming based boosting to perform the final decision regarding recognition. The learning of the system is efficient and effective. Also the performance measurement of the system in terms of accuracy, TPR, FPR and FNR of the classifier are substantially high and the recognition time of fingerprints are quite affordable.