Comparison of several classification algorithms for gender recognition from face images
Mutlu Sakarkaya, Fahrettin Yanbol, Zeyneb Kurt · 2012
This paper presents a comparison between several algorithms which were employed for gender recognition automatically. Firstly, the face images of various mature women and men samples were gathered, and face images were separated as train dataset and test dataset. Both of the datasets were pre-processed and made ready for following operations. Secondly, Principal Component Analysis (PCA) was applied to train dataset to extract the most distinguishing features. Finally, three classification algorithms, Support Vector Machine (SVM), k-Nearest Neighbourhood (k-NN), and Multivariate Classification with Multivariate Gauss Distribution (MCMGD) algorithms were implemented and compared to determine the most suitable and successful algorithm for gender recognition from face images. Experimental results illustrate that k-NN with k values 5, 7, 9 outperformed the other approaches.