improving gender recognition using fingerprint with svm, knn, and decision tree

In this paper, fingerprint gender recognition usinga combination of three feature vectors of knn, svm, anddecision tree was used to extract features to classify the gender ofpersons. fingerprint verification is one of the most reliable andcommon methods of identifying individuals and plays a veryimportant role in legal applications such as criminalinvestigations. fingerprint, on the other hand, is being used as abiometric tool to identify gender because of its unique characterand unchanging during person life. the most important featuresfrom knn, svm, and decision tree are used to classify afingerprint to male or female classes. the practical results showthat our proposed system can be used as a proper candidate incriminology with high accuracy compared to other strategies.