@article{oai:nagoya.repo.nii.ac.jp:00021696,
 author = {河野, 雄紀 and 高橋, 友和 and 出口, 大輔 and 井手, 一郎 and 村瀬, 洋 and KONO, Yuki and TAKAHASHI, Tomokazu and DEGUCHI, Daisuke and IDE, Ichiro and MURASE, Hiroshi},
 issue = {467},
 journal = {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},
 month = {Mar},
 note = {近年,監視カメラ画像中の人物識別に対する需要が高まっている.しかし,監視カメラから得られる人物の画像は,識別用顔画像と顔向きが異なる場合が多く,コンピュータで人物識別することは困難である.そこで,本報告では学習型局所画像変換に基づく顔向き変換手法を提案する.本手法を用いて,入力人物の顔向きを識別用顔画像の顔向きに変換することで顔認識が容易になる.提案手法は多数の人物の様々な角度の顔画像データセットを用いて,局所画像(パッチ)単位で顔画像を変換し,パッチを張り合わせることによって全体の顔画像を生成する.また,異なる人物間と角度間で同一部位が含まれるように,各パッチを対応付けることで,高精度な顔向き変換を実現する.入力角度を変えながら正面顔画像への変換を行い,人物識別実験を行ったところ,平均で77%の識別率が得られた., Recently, the demand for person identification in surveillance camera images is increasing. However, the facial pose of a person in surveillance camera images often differs from the facial pose in an person identification database. Therefore, it is difficult to identify a person in the image. In this report, we propose a method for facial poses transformation based on a learning-based local image transformation. It tackles face recognition across pose by virtually transforming a facial pose to the pose in the person identification database. The proposed method transforms facial poses using a face image dataset which consists of face images of a large number of individuals with various poses. I performs a patch-wise transformation, which transforms a partial image (patch) in a face at each location on the face. Also, it is necessary to find correspondences of patches between different poses and individuals so that the same part should be contained in the patches for achieving accurate pose transformation. A face recognition experiment was conducted with frontal face images transformed by the proposed method. As a result, the proposed method achieved a recognition rate of 77%., IEICE Technical Report;PRMU2010-263},
 pages = {145--150},
 title = {学習型局所画像変換に基づく顔向き変換手法に関する検討(一般セッション,文字・文書の認識と理解)},
 volume = {110},
 year = {2011}
}