Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes

• Item type: Trans(1)
• 公開日: 2009-09-24
• タイトル: Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes
• タイトル / 言語: en
• タイトル: Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Research Paper
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Science and Engineering, Ritsumeikan University
• 著者所属: Graduate School of Science and Engineering, Ritsumeikan University
• 著者所属: Graduate School of Science and Engineering, Ritsumeikan University
• 著者所属: Beauty Cosmetic Research Lab, Kao Corporation
• 著者所属: Beauty Cosmetic Research Lab, Kao Corporation
• 著者所属: Beauty Cosmetic Research Lab, Kao Corporation
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Ritsumeikan University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Ritsumeikan University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Ritsumeikan University
• 著者所属(英): en
• 著者所属(英): Beauty Cosmetic Research Lab, Kao Corporation
• 著者所属(英): en
• 著者所属(英): Beauty Cosmetic Research Lab, Kao Corporation
• 著者所属(英): en
• 著者所属(英): Beauty Cosmetic Research Lab, Kao Corporation
• 著者名: Xu, Qiao; Rui, Xu; Yen-WeiChen; Takanori, Igarashi; Keisuke, Nakao; Akio, Kashimoto
• 著者名(英): Xu, Qiao; Rui, Xu; Yen-Wei, Chen; Takanori, Igarashi; Keisuke, Nakao; Akio, Kashimoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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 highorder 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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 highorder 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.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12394973
• 書誌情報: IPSJ Transactions on Computer Vision and Applications（CVA）; 巻 1, p. 231-241, 発行日 2009-09-24
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会