Signal and Image Processing Letters
Vol. 1 No. 1: March 2019

Gender Classification using Fisherface and Support Vector Machine on Face Image

Muhammad Noor Fatkhannudin (Informatics Department, Universitas Ahmad Dahlan)
Adhi Prahara (Unknown)

Article Info

Publish Date
31 Mar 2019


Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.

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Journal Info





Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Engineering Neuroscience


The Signal and Image Processing Letters (SIMPLE) is an international, peer-reviewed, open access, online journal of applied research in the field of Signal and Image Processing. It is designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in the ...