@techreport{oai:ipsj.ixsq.nii.ac.jp:00216002,
 author = {久米, 孝 and 皆川, 純 and 山﨑, 賢人 and 阿倍, 博信 and Takashi, Kume and Jun, Minagawa and Kento, Yamazaki and Hironobu, Abe},
 issue = {22},
 month = {Jan},
 note = {近年，顔の 3 次元再構成への要求が高まっている．しかし，その品質は顔の向き，照明環境などによって左右される．そこで，様々な観点から 3 次元顔再構成のためにスコアリングすることで，最適な顔画像の抽出が可能となり，様々なシーンで顔の 3D モデルの活用が期待できる．このような背景のもと，筆者らは先行研究において正面画像との類似度に基づく顔画像のスコアリング方式を提案したが，着目する顔の特徴が不明瞭であった．そこで，本論文では，顔の姿勢がスコアリングに与える影響を評価するため，顔の向きと解像度にのみ着目した機械学習によるスコアリングモデルを生成する方式について述べる．, This paper describes a face image scoring method for 3D face reconstruction in the video surveillance fields. In recent years, 3D face reconstruction has been introduced to several fields, so the demand for it is increasing. However, qualities of reconstructed faces are affected by face orientation, facial expression, or environments. Considering this issue, we have proposed the scoring method which enables the video surveillance systems extract a best face image for the 3D face reconstruction from video frames. In our previous work, we proposed a method focused on the face similarities with frontal face images. However, it was unclear which feature this method is focusing on. Against this background, we consider the new method which evaluates scores using facial orientations and resolutions. In this paper, we describe a scoring model using machine learning and evaluations to clarify the effect of facial orientations and resolutions on scoring.},
 title = {映像監視システムにおける3次元姿勢推定に基づくベストショット抽出のための顔画像のスコアリング方式},
 year = {2022}
}