Gender classification using face recognition

Author

Bissoon, Terishka ; Viriri, Serestina

Author_Institution

Sch. of Math., Stat. & Comput. Sci., Univ. of KwaZulu-Natal Durban, Durban, South Africa

fYear

2013

fDate

25-27 Nov. 2013

Firstpage

1

Lastpage

4

Abstract

This paper addresses the issue of gender classification using the method of Principal Component Analysis (PCA) for face recognition and classification of human faces. The use of the PCA algorithm has a maximum success rate of 82%. The gender classification system is then improved by using the Linear Discriminant Analysis (LDA. This algorithm has a machine-learning framework by which it trains on a database and using this trained environment to predict the outcome of other images. The classification is restricted to two classes - male and female. Upon using LDA, the success rate increased to approximately 85%. The database used in this paper for the training and testing of images is called the FERET database.

Keywords

face recognition; image classification; learning (artificial intelligence); principal component analysis; FERET database; LDA; PCA algorithm; face recognition; gender classification system; human face classification; linear discriminant analysis; machine-learning framework; principal component analysis; Classification algorithms; Face; Face recognition; Feature extraction; Histograms; Principal component analysis; Training; LDA; PCA; gender classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Adaptive Science and Technology (ICAST), 2013 International Conference on

Conference_Location

Pretoria

Type

conf

DOI

10.1109/ICASTech.2013.6707489

Filename

6707489