顔画像からの感情と個人性のもつれ解き表現学習

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2021/09/01

タイトル(英語): Disentanglement Learning of Emotions and Identities from Facial Image

著者名: 相澤　宏旭（岐阜大学），村瀬　王哉（岐阜大学），加藤　邦人（岐阜大学）

著者名(英語): Hiroaki Aizawa (Gifu University), Kimiya Murase (Gifu University), Kunihito Kato (Gifu University)

キーワード: 表現学習，顔認識，感情認識　　representation learning，face recognition，emotion recognition

要約(英語): Learning disentangled representations of emotions and identities from facial images is important for understanding human faces. This is because the human face consists of emotions and identities, and the emotions affect the face as expressions. However, because these factors interact with each other, it is difficult to learn to decompose them from facial images. Such entangled representations lead to failure in facial recognition and facial image synthesis tasks. Therefore, we aim to disentangle emotions and identities from the facial image. To achieve this goal, we propose a novel method for learning disentangled representations of emotions and identities. In the experiment, we confirmed that our method achieves to learn disentangled representation of emotions and identities, and allows us to perform an emotion-controllable image synthesis by swapping two facial images.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.141 No.9 （2021） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 962-968 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/141/9/141_962/_article/-char/ja/