A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition

NCU Institutional Repository > 工學院 > 機械工程研究所 > 期刊論文 > Item 987654321/26826

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/26826

题名:	A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition
作者:	Hsieh,PC;Tung,PC
贡献者:	機械工程研究所
关键词:	COMPONENT ANALYSIS
日期:	2009
上传时间:	2010-06-29 18:01:09 (UTC+8)
出版者:	中央大學
摘要:	Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA 1) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images. we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods. (C) 2008 Elsevier Ltd. All rights reserved.
關聯:	PATTERN RECOGNITION
显示于类别:	[機械工程研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	762	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....