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Detection of Finger Contact with Skin Based on Shadows and Texture Around Fingertips

机译:基于指尖周围阴影和纹理的手指皮肤接触检测

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This paper proposes a method to detect contact between fingers and skin based on shadows and texture around fingertips. An RGB camera installed on a head-mounted display can use the proposed method to detect finger contact with the body. The processing pipeline of the method consists of extraction of fingertip image, image enhancement, and contact detection using machine learning. A fingertip image is extracted from a hand image to limit image features to those around fingertips. Image enhancement reduces the influence of different lighting environments. A contact detection utilizes deep learning models to achieve high accuracy. Datasets of fingertip images are built from videos recording where a user touches and releases the forearm with his/her fingers. An experiment is conducted to evaluate the proposed method in terms of image enhancement methods and data augmentation methods. Results of the experiment show that the proposed method has a maximum accuracy of 97.6% in cross-validation. The results also show that the proposed method is more robust to different users than different lighting environments.
机译:提出了一种基于指尖周围阴影和纹理的手指与皮肤接触检测方法。安装在头戴式显示器上的RGB摄像头可以使用所提出的方法检测手指与身体的接触。该方法的处理流程包括指尖图像的提取、图像增强和使用机器学习的接触检测。从手部图像中提取指尖图像,以将图像特征限制为指尖周围的特征。图像增强可减少不同照明环境的影响。接触检测利用深度学习模型实现高精度。指尖图像的数据集是从用户用手指触摸和释放前臂的视频记录中建立的。实验从图像增强方法和数据增强方法两个方面对该方法进行了评价。实验结果表明,该方法在交叉验证中的最大准确率为97.6%。结果还表明,与不同的光照环境相比,该方法对不同的用户具有更强的鲁棒性。

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