An advance approach Of PCA for gender recognition

Author

Kumar, Ajit ; Rawat, Karun ; Gupta, Deepika

Author_Institution

Gov. Eng. Coll., Ajmer, India

fYear

2013

fDate

21-22 Feb. 2013

Firstpage

59

Lastpage

63

Abstract

This paper focuses on mathematical rigor to provide explicit solution for gender recognition by extracting feature vector. This paper implement face recognition system using Principal Component Analysis (PCA) algorithm. In addition by using face-rec database we will use kernel SVM to find k Eigen for which error of classification is smallest then project data points along these vector to reduce dimensionality.

Keywords

face recognition; feature extraction; gender issues; image classification; principal component analysis; support vector machines; visual databases; PCA algorithm; advance PCA approach; classification error; dimensionality reduction; face recognition system; face-rec database; feature vector extraction; gender recognition; k Eigen; kernel SVM; principal component analysis algorithm; Face; Face recognition; Feature extraction; Principal component analysis; Support vector machines; Training; Vectors; Eigen faces; Euclidian distance; Gender Recognition; Principal Component Analysis; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Communication and Embedded Systems (ICICES), 2013 International Conference on

Conference_Location

Chennai

Print_ISBN

978-1-4673-5786-9

Type

conf

DOI

10.1109/ICICES.2013.6508195

Filename

6508195