Improving Gender Recognition Using Fingerprint with SVM, KNN, and Decision Tree

In this paper, fingerprint gender recognition using a combination of three feature vectors of KNN, SVM, and decision tree was used to extract features to classify the gender ofpersons. Fingerprint verification is one of the most reliable and common methods of identifying individuals and plays a very important role in legal applications such as criminal investigations. Fingerprint, on the other hand, is being used as a biometric tool to identify gender because of its unique character and unchanging during person life. The most important features from KNN, SVM, and decision tree are used to classify a fingerprint to male or female classes. The practical results show that our proposed system can be used as a proper candidate in criminology with high accuracy compared to other strategies.

کلیدواژه ها:

Fingerprint ، Classification ، Gender Recognition ، Decision Tree ، KNN ، SVM