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¹Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)

|October 16, 2025

概括

这项研究引入了一种新的面部手势识别系统,用于无接触式控制自动驾驶系统. 幽灵网-BiLSTM-注意力 (GBA) 方法达到99.13%的准确性,使直观的人机交互成为可能.

科学领域:

机器人技术机器人技术机器人技术
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

传统的控制系统依赖于物理设备,限制了无接触式交互.
人工智能和计算机视觉的进步使面部手势识别能够用于人机交互.

研究的目的:

为运行自主系统开发一种轻量级的实时面部手势识别系统.
将系统与3D机器人模拟进行集成,以实现沉浸式控制.

主要方法:

提出了一个GhostNet-BiLSTM-Attention (GBA) 模型用于面部手势识别.
在FaceGest数据集上训练了GBA模型.
通过插座通信将系统与Unity 3D机器人模拟集成在一起.

主要成果:

在FaceGest数据集上实现了99.13%的分类准确性.
在实时评估中展示了高精度和低推断延迟.
在各种用户和照明条件下展示了强度.

结论:

GBA系统为无接触式机器人控制提供了准确有效的方法.
面部手势识别为人机交互提供了一个直观的界面.
潜在的应用包括辅助机器人,远程操作和沉浸式接口.

关键词:

幽灵网络-BiLSTM-注意事项模拟单位的模拟.人与机器人的互动实时识别实时识别时间面部手势识别功能无触摸的无触摸控制器

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