@techreport{oai:ipsj.ixsq.nii.ac.jp:00225923,
 author = {久保, 智哉 and 横川, 智教 and 有本, 和民 and 穂苅, 真樹 and 茅野, 功 and Tomoya, Kubo and Kazutami, Arimoto and Yokogawa, Tomoyuki and Isao, Kayano4},
 issue = {51},
 month = {May},
 note = {我々はドライバーの顔全体の印象から眠気を予測する顔表情評定 AI システムを開発している．本研究では，車載用組込みマイコンボード上で動作することを目指し，2D-CNN モデルによる顔表情評定 AI システムを開発しているが，汎化性能向上が課題となっている．本稿では汎化性能向上に向けた研究成果について述べる．, We are currently developing a Face Texture Analysis AI system that predicts driver drowsiness based on the overall impression of the driver's face. Our goal is to create a system and operates on an embedded microcontroller board specifically designed for use in vehicles. However, we have identified that improving the generalization performance of the system poses a significant challenge. Therefore, the purpose of this paper is to present our research findings aimed at improving the generalization performance of our f Face Texture Analysis AI system for predicting driver drowsiness.},
 title = {ドライバーの眠気予測を目的とした顔表情評定AIシステムの汎化性能向上に向けての検討},
 year = {2023}
}