Chang Victor, Eniola Rahman Olamide, Golightly Lewis, Xu Qianwen Ariel
Aston University, Aston St, Birmingham, B4 7ET UK.
Teesside University, Campus Heart, Southfield Rd, Middlesbrough, TS1 3BX UK.
SN Comput Sci. 2023;4(5):441. doi: 10.1007/s42979-023-01751-y. Epub 2023 Jun 12.
Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human-computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human-computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system's algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system's second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.
科学家们正在开发手势识别系统，以在无需额外设备的情况下改善真实、高效且轻松的人机交互，特别是对于依赖手势作为唯一交流方式的言语障碍群体。不幸的是，在大多数人机交互研究中，如自然语言处理和其他自动化领域，言语障碍群体的代表性不足，这使得他们更难通过这些先进系统与系统及他人进行交互。该系统的算法分为两个阶段。第一步是基于颜色空间分割技术的感兴趣区域分割，通过预设的颜色范围将感兴趣区域的像素（手）与背景（不在所需感兴趣区域的像素）区分开来。系统的第二阶段是将分割后的图像输入卷积神经网络（CNN）模型进行图像分类。对于图像训练，我们使用了Python的Keras包。该系统证明了手势识别中图像分割的必要性。最优模型的性能为58%，比未进行图像分割时获得的准确率高出约10%。
