<![CDATA[Nowadays face recognition still being a hot topics to be discussed especially it’s utility for genderclassification. Gender classification is an easy task for human but it’s a challenging task for computersbecause it doesn’t have capability for recognizing human gender without feature extraction. There arestill many researches about facial image feature extraction for gender classification. Geometryfeatures and Texture Features are well perform features for gender classification. This paper willpresents fusion of those feature in order to find an improvement for gender classifications task. Eachfeatures will be extracted using Viola Jones Algorithm and Compass Local Binary Pattern method.Both features will be combined using concatenated method in dataframe format. Viola Jonesalgorithm has an issues when detecting each facial regions so it causes outliers in each geometryfeatures. The proposed method will be evaluated on color FERET dataset that contains facial images.Classification task will be done using Random Forest and Backpropagation. The result is fusionfeatures present an improvement in gender classification using Backpropagation with 87% accuracy.It prove that proposed method perform better than single feature extraction method.]]>
Copyrights © 2024
