College of Mechanical Engineering, North University of China, Taiyuan 030051, China.
SIMITECH Co., Xi'an 710086, China.
Sensors (Basel). 2024 Sep 27;24(19):6262. doi: 10.3390/s24196262.
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.
手势识别在人与人以及人与机器的交互中起着重要作用。目前，大多数手势检测方法都依赖于固定的手势识别。然而，日常生活中手势的多样性和可变性，本文提出了一种基于三重损失的可注册手势识别方法。通过学习不同手势之间的差异，它可以对其进行聚类并识别新添加的手势。本文构建了一个可注册手势数据集（RGDS），用于训练可注册手势识别模型。此外，它提出了一种用于转换手势数据的归一化方法和一个 FingerComb 块，用于组合和提取手势数据，以增强特征并加速模型收敛。它还改进了 ResNet 并引入了 FingerNet 用于可注册单手手势识别。所提出的模型在 RGDS 数据集上表现良好。该系统是可注册的，允许用户灵活地注册自己的手势，实现个性化的手势识别。
