HVPN: The combination of horizontal and vertical pose normalization for face recognition

ASIAIR > College of Computer Science > Department of Computer Science and Information Engineering > Proceedings > Item 310904400/8832

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8832

Title:	HVPN: The combination of horizontal and vertical pose normalization for face recognition
Authors:	Gu, Hui-Zhen;Kao, Yung-Wei;Lee, Suh-Yin;Yuan, Shyan-Ming
Contributors:	Department of Computer Science and Information Engineering
Keywords:	Discriminant analysis;Principal component analysis;Face database;Face recognition algorithms;Face recognition systems;Linear discriminant analysis;Pose invariants;Pose normalization;Principal components;Recognition performance;Reference models
Date:	2009
Issue Date:	2010-04-08 12:22:10 (UTC+0)
Publisher:	Asia University
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.
Relation:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5371 :367-378
Appears in Collections:	[Department of Computer Science and Information Engineering] Proceedings

Files in This Item:

File	Description	Size	Format
		0Kb	Unknown	463	View/Open
67.doc		30Kb	Microsoft Word	186	View/Open

Loading...