1. Real-Time Hand Gesture Monitoring Model Based on MediaPipe’s Registerable System
- Author
-
Yuting Meng, Haibo Jiang, Nengquan Duan, and Haijun Wen
- Subjects
machine learning ,real-time monitoring ,gesture recognition ,Triple Loss ,Chemical technology ,TP1-1185 - Abstract
Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Additionally, it proposes a normalization method for transforming hand gesture data and a FingerComb block for combining and extracting hand gesture data to enhance features and accelerate model convergence. It also improves ResNet and introduces FingerNet for registerable single-hand gesture recognition. The proposed model performs well on the RGDS dataset. The system is registerable, allowing users to flexibly register their own hand gestures for personalized gesture recognition.
- Published
- 2024
- Full Text
- View/download PDF