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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

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/2237/14960
500796b6-2eef-4f77-94b3-65ba7d6eb4a8
名前 / ファイル ライセンス アクション
356.pdf 356.pdf (1.3 MB)
Item type 学術雑誌論文 / Journal Article(1)
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

WEKO 41102

en Lina

Search repository
TAKAHASHI, Tomokazu

× TAKAHASHI, Tomokazu

WEKO 41103

en TAKAHASHI, Tomokazu

Search repository
IDE, Ichiro

× IDE, Ichiro

WEKO 41104

en IDE, Ichiro

Search repository
MURASE, Hiroshi

× MURASE, Hiroshi

WEKO 41105

en MURASE, Hiroshi

Search repository
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
言語 en
権利情報 Copyright (C) 2009 IEICE
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 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
内容記述タイプ Abstract
出版者
言語 en
出版者 Institute of Electronics, Information and Communication Engineers
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
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
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