Recognition of facial images using support vector machines

Author

Kim, K.I. ; Kim, J. ; Jung, K.

Author_Institution

A.I. Lab, Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

468

Lastpage

471

Abstract

A novel support vector machine (SVM)-based method for appearance-based face recognition is presented. The proposed method does not use any external feature extraction process. Accordingly the intensities of the raw pixels that make up the face pattern are fed directly to the SVM. However, it takes account of prior knowledge about facial structures in the form of a kernel embedded in the SVM architecture. The new kernel efficiently explores spatial relationships among potential eye, nose, and mouth objects and is compared with existing kernels. Experiments with the ORL database show a recognition rate of 98% and speed of 0.22 seconds per face with 40 classes

Keywords

face recognition; feature extraction; learning automata; ORL database; SVM architecture; appearance-based face recognition; eye objects; facial images recognition; kernel; mouth objects; nose objects; raw pixels; support vector machines; Face recognition; Feature extraction; Image recognition; Kernel; Mouth; Nose; Pattern recognition; Polynomials; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955324

Filename

955324