Face Recognition Based on Binary Edge Map and Support Vector Machine

Author

Qiu, Xuena ; Wang, Wei ; Song, Jiatao ; Zhang, Xuejun ; Liu, Shirong

Author_Institution

Coll. of Electron. & Inf. Eng., Ningbo Univ. of Technol., Ningbo

fYear

2007

fDate

6-8 July 2007

Firstpage

519

Lastpage

522

Abstract

In this paper, a novel face recognition scheme is presented. Our method uses binary edge map (BEM) as face representation and employs support vector machine (SVM) as face classifiers. The BEM is extracted using locally adaptively threshold (LAT) method. Experimental results show that a face recognition rate of 92.73% can be achieved on 165 Yale face images and 95.62% can be achieved on 798 AR images, indicating that the proposed approach is robust to lighting changes.

Keywords

face recognition; support vector machines; Yale face images; binary edge map; face recognition; locally adaptively threshold; support vector machine; Data mining; Educational institutions; Face recognition; Independent component analysis; Lighting; Robustness; Spatial databases; Support vector machine classification; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on

Conference_Location

Wuhan

Print_ISBN

1-4244-1120-3

Type

conf

DOI

10.1109/ICBBE.2007.136

Filename

4272620

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3049092