頭部姿勢変動に頑健なまぶた及び黒目の追跡手法

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: 2014/05/01

タイトル(英語): Head-pose Invariant Eyelid and Iris Tracking Method

著者名: 田村　仁優（慶應義塾大学理工学研究科），橋本　潔（慶應義塾大学理工学研究科），青木　義満（慶應義塾大学理工学研究科）

著者名(英語): Kimimasa Tamura （Graduate School of Science and Technology, Keio University）, Kiyoshi Hashimoto （Graduate School of Science and Technology, Keio University）, Yoshimitsu Aoki （Graduate School of Science and Technology, Keio University）

キーワード: 視線推定，まぶた追跡，黒目追跡，ASM，インターフェース，3次元再構成　　Gaze Estimation，Eyelid Tracking，Iris Tracking，ASM，Interface，3D Reconstruction

要約(英語): These days, there is more demand of camera based gaze estimation method for a new interface and a new marketing measurement tool. Considering these applications, the system should track a new user without any operation like calibrations. It also admits user's natural head pose changes. Previous methods, however, need calibration procedure before execution and have less accuracy under head moving situation. In this paper, we propose the method which tracks user's eyelid and iris automatically and accurately. Our method is the pretreatment of gaze estimation without any calibration and head pose restraint. First of all we track the facial feature points from an input face image and estimate its head pose, extracting eye region image. On the eye region image, we track eyelid shape based on the eyelid shape model generated beforehand from PCA. Finally we track iris inside the eyelid based on the eye ball model. These eyelid and iris tracking are processed by Particle Filter. From the evaluation of database including head pose changes, we confirmed that accuracy of the eyelid and iris tracking is improved compared with previous methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.134 No.5 （2014） 特集：機械学習手法に基づく設備診断・監視技術

本誌掲載ページ: 694-701 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/134/5/134_694/_article/-char/ja/