3次元畳み込みニューラルネットワークをもちいた瞬目種類識別の詳細分析

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: 2023/09/01

タイトル(英語): Detailed Analysis of Blink Types Classification Using a 3D Convolutional Neural Network

著者名: 佐藤　寛修（関東学院大学理工学部），阿部　清彦（東京電機大学システムデザイン工学部），松野　省吾（群馬大学情報学部），大山　実（東京電機大学システムデザイン工学部）

著者名(英語): Hironobu Sato (College of Science and Engineering, Kanto Gakuin University), Kiyohiko Abe (School of System Design and Technology, Tokyo Denki University), Shogo Matsuno (Faculty of Informatics, Gunma University), Minoru Ohyama (School of System Design and Technology, Tokyo Denki University)

キーワード: 3次元畳み込みニューラルネットワーク，動作認識，瞬目計測，画像処理，入力インタフェース　　3D convolutional neural network，action recognition，eye-blink measurement，image processing，input interface

要約(英語): In the development of blink input interfaces, it is important to classify between conscious (voluntary) and naturally occurring (involuntary) blinks. In the previous studies, some systems employed a long blink as a voluntary blink, but determining the appropriate discriminative condition was difficult. To avoid the problem of individual differences in discrimination conditions, an individual calibration method was proposed, but the calibration procedure increases the burden on the user. In this study, we introduce a new 3D convolutional neural network (3D CNN), which deals with spatial and temporal dimensional directions. This 3D CNN model is trained with a moving image dataset of the periocular area. For the proposed 3D CNN, a sequence of seven images cut from a video sequence is used as a set of an input sample to classify three states; voluntary, involuntary, and not blinking. In this study, data of five subjects were used for training and seven for testing. A detailed analysis of the result revealed that the biased position of the open-eye area in the images leads to a lower classification rate. To address this problem, we propose an automatic determination method for the area to be cropped in the periocular image and verify its performance.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.9 （2023） 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: 971-978 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/143/9/143_971/_article/-char/ja/