چکیده مقاله
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
کلیدواژهها
نویسندگان
Kimia Shirini
Computer Science, Eng Dept Tabriz University Tabriz, Iran
Nafiseh Roshan Zamir
Computer Science, Eng Dept Tabriz University Tabriz, Iran
Mohammad Ahmadi Ganjei
Computer Science, Eng, IT Dept Shiraz University Shiraz, Iran
Mohammad-Reza Feizi-Derakhshi
Computer Science, Eng Dept Tabriz University Tabriz, Iran
شیوه ارجاع
Shirini, Kimia and Roshan Zamir, Nafiseh and Ahmadi Ganjei, Mohammad and Feizi-Derakhshi, Mohammad-Reza,1398,Improving Gender Recognition Using Fingerprint with SVM, KNN, and Decision Tree,3rd national conference on Computer, Information Technology and Artificial Intelligence,Ahvaz
ارائهشده در
مجموعه مقالات سومین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی16 بهمن 1398 · اهواز