Face Recognition Based on Face Gabor Image and SVM

Author

Wang, Xiao-ming ; Huang, Chang ; Ni, Guo-Yu ; Liu, Jin-gao

Author_Institution

Sch. of Inf. Sci. & Technol., East China Normal Univ., Shanghai, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

The paper proposes an effective algorithm for face recognition using face Gabor image and support vector machine (SVM). The face Gabor image is firstly derived by downsampling and concatenating the Gabor wavelets representations which are the convolution of the face image with a family of Gabor kernels, and then the 2D principle component analysis (2DPCA) method is applied to the face Gabor image to extract the feature space. Finally, support vector machine (SVM) is used to classify. Experimental results on ORL database show that the face Gabor image carries more discriminant information and the proposed method can achieve 99.5% recognition rate on full face dataset and achieve 98.0% recognition rate on unitary dataset.

Keywords

Gabor filters; convolution; face recognition; feature extraction; image classification; image representation; image sampling; principal component analysis; support vector machines; wavelet transforms; 2D principle component analysis method; 2DPCA method; Gabor kernel; Gabor wavelet representation; ORL database; SVM; discriminant information; face Gabor image recognition algorithm; face image convolution; feature space extraction; image classification; image downsampling; support vector machine; unitary dataset; Convolution; Data mining; Face recognition; Feature extraction; Image analysis; Image recognition; Kernel; Support vector machine classification; Support vector machines; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5301800

Filename

5301800