Comparison of feature extraction and feature selection approaches to decide whether a face image belongs to a male or a female

Engin Semih Basmaci, Ulas Kaymakcioglu, Zeyneb Kurt

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

14 Citations (Scopus)

Abstract

In this study, a gender recognition system which only uses face images was proposed. Since the dimension of the face images were huge and different from each other; the number of features should be decreased. In order to decrease the dimension of the images Principal Component Analysis (PCA) and a hybrid aprproach combined by PCA+SFS (Sequential Forward Selection) has been presented and their performances were compared with each other. Via PCA and PCA+SFS hybrid method, the dimension of the dataset was reduced and the proposed system was trained and tested by Support Vector Machine (SVM). The classification results of two dimension reduction approaches according to the extracted features were evaulated via SVM (Support Vector Machines) and the classification results were compared.
Original languageEnglish
Title of host publication2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages522-525
Number of pages4
ISBN (Print)9781457704628
DOIs
Publication statusPublished - 20 Apr 2011
Externally publishedYes
Event2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 - Antalya, Turkey
Duration: 20 Apr 201122 Apr 2011

Publication series

Name2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011

Conference

Conference2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
Country/TerritoryTurkey
CityAntalya
Period20/04/1122/04/11

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