Comparison of several classification algorithms for gender recognition from face images

Mutlu Sakarkaya*, Fahrettin Yanbol, Zeyneb Kurt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationINES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings
Pages97-101
Number of pages5
DOIs
Publication statusPublished - 1 Oct 2012
Externally publishedYes
EventIEEE 16th International Conference on Intelligent Engineering Systems, INES 2012 - Lisbon, Portugal
Duration: 13 Jun 201215 Jun 2012

Publication series

NameINES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

Conference

ConferenceIEEE 16th International Conference on Intelligent Engineering Systems, INES 2012
CountryPortugal
CityLisbon
Period13/06/1215/06/12

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