{"_buckets": {"deposit": "a551c550-44a7-4013-8f06-9a9be52461e4"}, "_deposit": {"id": "21711", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21711"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021711", "sets": ["314"]}, "author_link": ["64437", "64438", "64439", "64440", "64441", "64442", "64443", "64444", "64445", "64446"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A Study on Gaze Estimation from Person Images Using Head Pose Information", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-03", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "495", "bibliographicPageEnd": "104", "bibliographicPageStart": "99", "bibliographicVolumeNumber": "112", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. PRMU, パターン認識・メディア理解", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "近年,カメラを用いて人物の視線方向を推定する技術が注目されている.しかし,実環境下で得られる人物画像は,低解像度であるほか,遮蔽や照明変化などを含むことが多く,高品質な目領域画像を必要とする従来手法の適用は難しい.そこで本研究では,体に対する顔の向きと顔の動きから視線方向を推定する手法を提案する.提案手法では,まず人物動画像から姿勢情報を推定し,その結果に基づいて視線方向の尤度関数を計算する.そして,尤度が最大となる視線方向を推定結果として出力する.評価実験において,提案手法と従来手法の視線推定誤差を比較することにより,提案手法の有効性を確認した.", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "Gaze estimation techniques using a camera have widely been studied in recent years. Most existing methods require a high-quality image of the eye area. However, an eye image obtained under real environments is often in low resolution, affected by illumination changes, and occluded by sunglasses or head rotation. Thus, we propose a method that estimates a gaze direction accurately using pose information including the head direction, the body direction, and the head motion. The proposed method estimates the pose information from an input person image sequence. Then, it calculates a likelihood function of gaze directions using the estimated pose information. Finally, it outputs the gaze direction that maximizes the likelihood value as a gaze estimation result. Experimental results showed that the proposed method could estimate gaze directions more accurately than an existing method. This indicates the effectiveness of the proposed method.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "IEICE Technical Report;PRMU2012-197", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://ci.nii.ac.jp/naid/110009713422/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23860"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人電子情報通信学会", "subitem_publisher_language": "ja"}]}, "item_10_relation_40": {"attribute_name": "シリーズ", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "IEICE Technical Report;PRMU2012-197"}]}]}, "item_10_relation_43": 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