Improving Gender Recognition Using Fingerprint with SVM, KNN, and Decision Tree
Published in 3rd national conference on Computer, Information Technology and Artificial Intelligence, 2020
Recommended citation: K.Shirini, N.R.Zamir M.A.Ganjei, MR.Feizi-Derakhshi(2020). "Improving Gender Recognition Using Fingerprint with SVM, KNN, and Decision Tree." 3rd national conference on Computer, Information Technology and Artificial Intelligence. https://en.civilica.com/doc/1015568/
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 of persons. Fingerprint verification is one of the most reliable and standard methods of identifying individuals and plays a vital role in legal applications such as criminal investigations. On the other hand, Fingerprint is being used as a biometric tool to identify gender because of its unique character and unchanging during a person’s life. The essential features from KNN, SVM and decision tree are used to classify a fingerprint into 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.