A non linear face recognition system using Support Vector Machine

Author

Sani, Maizura Mohd ; Samad, Salina Abdul ; Ishak, Khairul Anuar

Author_Institution

Center for Comput. Eng. Studies, Univ. Teknol. Mara Shah Alam, Shah Alam, Malaysia

fYear

2012

fDate

23-25 March 2012

Firstpage

48

Lastpage

51

Abstract

A face recognition system uses face to verify individuals using computing capability. However, its performances often degrade due to high dimensional data and large feature appearance of the face image. This paper present a face recognition system based on non linear feature extraction technique to reduce the dimensionality of the face image, called Locally Linear Embedding. This method considers the hidden layer of face manifold to be the input of a SVM multiclass classifier. The performance is evaluated using the ORL database and achieved better recognition rates than the Principal Component Analysis.

Keywords

face recognition; feature extraction; support vector machines; ORL database; SVM multiclass classifier; face image dimensionality; face manifold; high dimensional data; large feature appearance; locally linear embedding; nonlinear face recognition system; nonlinear feature extraction; support vector machine; Databases; Face; Face recognition; Principal component analysis; Support vector machines; Testing; Training; Locally Linear Embedding; Support Vector Machine; face recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on

Conference_Location

Melaka

Print_ISBN

978-1-4673-0960-8

Type

conf

DOI

10.1109/CSPA.2012.6194689

Filename

6194689