Accession number:20090611898950
Title: HVPN: The combination of horizontal and vertical pose normalization for face recognition
Authors: Gu, Hui-Zhen (1); Kao, Yung-Wei (1); Lee, Suh-Yin (1); Yuan, Shyan-Ming (1)
Author affiliation:(1) Department of Computer Science and
Engineering, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 300, Taiwan; (2) Department of Computer Science and Engineering, Asia University, Lioufeng Rd., Wufeng, Taichung County, Taiwan
Corresponding author:Gu, H.-Z.
(hcku@cs.nctu.edu.tw)
Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics)
Abbreviated source title:Lect. Notes Comput. Sci.
Volume:5371 LNCS
Monograph title:Advances in Multimedia Modeling - 15th International Multimedia Modeling Conference, MMM 2009, Proceedings
Issue date:2009
Publication year:2009 Pages:367-378
Language:English ISSN:03029743 E-ISSN:16113349 ISBN-10:354092891X ISBN-13:9783540928911
Document type:Conference article (CA)
Conference name:15th International Multimedia Modeling Conference, MMM 2009
Conference date:January 7, 2009 - January 9, 2009 Conference location:Sophia-Antipolis, France
Conference code:75294
Publisher:Springer Verlag, Tiergartenstrasse 17, Heidelberg, D- 69121, Germany
Abstract:Face recognition has received much attention with
numerous applications in various fields. Although many face recognition algorithms have been proposed, usually they are not highly accurate enough when the poses of faces vary considerably.
In order to solve this problem, some researches have proposed pose normalization algorithm to eliminate the negative effect cause by poses. However, only horizontal normalization has been considered in these researches. In this paper, the HVPN (Horizontal and Vertical Pose Normalization) system is proposed to accommodate the pose problem effectively. A pose invariant reference model is re-rendered after the horizontal and vertical pose normalization sequentially. The proposed face recognition system is evaluated based on the face database constructed by our self. The experimental results
demonstrate that pose normalization can improve the recognition performance using conventional principal component analysis (PCA) and linear discriminant analysis (LDA) approaches under varying pose. Moreover, we show that the combination of horizontal and vertical pose normalization can be evaluated with higher
performance than mere the horizontal pose normalization. ©
2008 Springer Berlin Heidelberg.
Number of references:12 Main heading:Face recognition
Controlled terms: Discriminant analysis - Principal component analysis
Uncontrolled terms: Face database - Face recognition algorithms - Face recognition systems - Linear discriminant analysis - Pose
invariants - Pose normalization - Principal components - Recognition performance - Reference models
Classification code:716 Telecommunication; Radar, Radio and
Television - 723.5 Computer Applications - 741.1 Light/Optics - 903.1 Information Sources and Analysis - 922 Statistical Methods - 922.2 Mathematical Statistics
DOI:10.1007/978-3-540-92892-8_38 Database:Compendex
Compilation and indexing terms, Copyright 2009 Elsevier Inc.
