Çamalan, SedaCamalan,S.Sengul,G.Şengül, GökhanInformation Systems EngineeringComputer Engineering2024-07-052024-07-0520163978-150901679-210.1109/SIU.2016.74962012-s2.0-84982817013https://doi.org/10.1109/SIU.2016.7496201https://hdl.handle.net/20.500.14411/3788In this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed and the comparison results are shown. These methods are applied on FERET database with 530 female and 731 male images. To have better performance, the face parts of the images are cropped then feature extraction and classification methods applied on the face part of the images. © 2016 IEEE.trinfo:eu-repo/semantics/closedAccessClassificationDiscriminant Analysisfeature extractionGender PredictionKNNLBPConference Object21612164