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Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences
http://hdl.handle.net/2237/14960
http://hdl.handle.net/2237/14960500796b6-2eef-4f77-94b3-65ba7d6eb4a8
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
356.pdf (1.3 MB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2011-06-28 | |||||
| タイトル | ||||||
| タイトル | Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences | |||||
| 言語 | en | |||||
| 著者 |
Lina
× Lina× TAKAHASHI, Tomokazu× IDE, Ichiro× MURASE, Hiroshi |
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| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 権利 | ||||||
| 権利情報 | Copyright (C) 2009 IEICE | |||||
| 言語 | en | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | appearance manifold | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | view-dependent covariance matrix | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | incremental learning | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | video-based face recognition | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | eigenspace | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | We propose an appearance manifold with view-dependent covariance matrix for face recognition from video sequences in two learning frameworks: the supervised-learning and the incremental unsupervised-learning. The advantages of this method are, first, the appearance manifold with view-dependent covariance matrix model is robust to pose changes and is also noise invariant, since the embedded covariance matrices are calculated based on their poses in order to learn the samples' distributions along the manifold. Moreover, the proposed incremental unsupervised-learning framework is more realistic for real-world face recognition applications. It is obvious that it is difficult to collect large amounts of face sequences under complete poses (from left sideview to right sideview) for training. Here, an incremental unsupervised-learning framework allows us to train the system with the available initial sequences, and later update the system's knowledge incrementally every time an unlabelled sequence is input. In addition, we also integrate the appearance manifold with view-dependent covariance matrix model with a pose estimation system for improving the classification accuracy and easily detecting sequences with overlapped poses for merging process in the incremental unsupervised-learning framework. The experimental results showed that, in both frameworks, the proposed appearance manifold with view-dependent covariance matrix method could recognize faces from video sequences accurately. | |||||
| 言語 | en | |||||
| 出版者 | ||||||
| 出版者 | Institute of Electronics, Information and Communication Engineers | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||
| タイプ | journal article | |||||
| 出版タイプ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
| 関連情報 | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://www.ieice.org/jpn/trans_online/index.html | |||||
| ISSN | ||||||
| 収録物識別子タイプ | PISSN | |||||
| 収録物識別子 | 0916-8532 | |||||
| 書誌情報 |
en : IEICE transactions on information and systems 巻 E92-D, 号 4, p. 642-652, 発行日 2009-04-01 |
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| 著者版フラグ | ||||||
| 値 | publisher | |||||
| URI | ||||||
| 識別子 | http://www.ieice.org/jpn/trans_online/index.html | |||||
| 識別子タイプ | URI | |||||
| URI | ||||||
| 識別子 | http://hdl.handle.net/2237/14960 | |||||
| 識別子タイプ | HDL | |||||
