- 著者
-
XU@QIAO
XU RUI
CHEN YEN-WEI
Igarashi Takanori
Nakao Keisuke
Kashimoto Akio
- 出版者
- 一般社団法人 情報処理学会
- 雑誌
- 情報処理学会論文誌 論文誌トランザクション (ISSN:18827772)
- 巻号頁・発行日
- vol.1, pp.231-241, 2009
- 被引用文献数
-
2
6
This paper introduces a framework called generalized N-dimensional principal component analysis (GND-PCA) for statistical appearance modeling of facial images with multiple modes including different people, different viewpoint and different illumination. The facial images with multiple modes can be considered as high-dimensional data. GND-PCA can represent the high-order dimensional data more efficiently. We conduct extensive experiments on MaVIC Database (KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database) to evaluate the effectiveness of the proposed algorithm and compared the conventional ND-PCA in terms of reconstruction error. The results indicated that the extraction of data features is computationally more efficient using GND-PCA than PCA and ND-PCA.