模式识别与人工智能
Thursday, Apr. 3, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2010, Vol. 23 Issue (4): 477-482    DOI:
Orignal Article Current Issue| Next Issue| Archive| Adv Search |
Face Recognition Based on Enhanced Gabor Feature and Direct Fractional-Step Linear Discriminant Analysis
ZOU Jian-Fa1, WANG Guo-Yin1, GONG Xun2
1.Institute of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065
2.School of Information Science and Technology,Southwest Jiaotong University,Chengdu 610031

Download: PDF (425 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  Gabor features can effectively represent the local features of face image with different directions and scales. However, traditional Gabor features based algorithms neglect the global features of the original image. Enhanced Gabor features (EGF) is developed in this paper by combining Gabor features and information extracted from the original image. A face recognition method is further proposed based on EGF and direct fractional-step linear discriminant analysis algorithm (DF_LDA). Experiment results of simulation on Yale, ORL and Georgia face databases show that EGF can effectively improve the face recognition rate compared with the traditional Gabor features.
Key wordsFace Recognition      Feature Selection      Gabor Feature      Direct Fractional-Step Linear Discriminant Analysis     
Received: 12 May 2009     
ZTFLH: TP391.4  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
ZOU Jian-Fa
WANG Guo-Yin
GONG Xun
Cite this article:   
ZOU Jian-Fa,WANG Guo-Yin,GONG Xun. Face Recognition Based on Enhanced Gabor Feature and Direct Fractional-Step Linear Discriminant Analysis[J]. , 2010, 23(4): 477-482.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2010/V23/I4/477
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn