{"_buckets": {"deposit": "f9c7c976-f3b7-42e1-94e5-9ee45170515a"}, "_deposit": {"id": "13066", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "13066"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00013066"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2009-04-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "4", "bibliographicPageEnd": "652", "bibliographicPageStart": "642", "bibliographicVolumeNumber": "E92-D", "bibliographic_titles": [{"bibliographic_title": "IEICE transactions on information and systems"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "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\u0027 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\u0027s 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.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://www.ieice.org/jpn/trans_online/index.html"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/14960"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "Institute of Electronics, Information and Communication Engineers"}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright (C) 2009 IEICE"}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "0916-8532", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Lina"}], "nameIdentifiers": [{"nameIdentifier": "41102", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "TAKAHASHI, Tomokazu"}], "nameIdentifiers": [{"nameIdentifier": "41103", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "IDE, Ichiro"}], "nameIdentifiers": [{"nameIdentifier": "41104", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "MURASE, Hiroshi"}], "nameIdentifiers": [{"nameIdentifier": "41105", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "356.pdf", "filesize": [{"value": "1.3 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1300000.0, "url": {"label": "356.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/13066/files/356.pdf"}, "version_id": "afcf10f9-0fc6-40e9-b6d4-c9437487906c"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "appearance manifold", "subitem_subject_scheme": "Other"}, {"subitem_subject": "view-dependent covariance matrix", "subitem_subject_scheme": "Other"}, {"subitem_subject": "incremental learning", "subitem_subject_scheme": "Other"}, {"subitem_subject": "video-based face recognition", "subitem_subject_scheme": "Other"}, {"subitem_subject": "eigenspace", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/14960", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2011-06-28"}, "publish_date": "2011-06-28", "publish_status": "0", "recid": "13066", "relation": {}, "relation_version_is_last": true, "title": ["Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences"], "weko_shared_id": 3}