School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.
Sensors (Basel). 2021 Sep 29;21(19):6525. doi: 10.3390/s21196525.
Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover's distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.
手势识别已经研究了几十年，但仍然是一个未解决的问题。其中一个重要原因是表示这些手势的特征不足，这可能导致性能不佳和鲁棒性差。因此，这项工作旨在为手手势识别提供全面而有区别的特征。在此，在 Kinect 传感器平台上提出了一种独特的指尖梯度方向与手指傅里叶（FGFF）描述符和改进的 Hu 矩。首先，设计了两种算法来提取指尖增强特征，包括手掌中心、指尖及其梯度方向，然后使用手指增强傅里叶描述符构建 FGFF 描述符。然后，讨论了具有更低指数的修改后的 Hu 不变矩来编码手部区域中轮廓增强的结构。最后，基于手指欧几里得距离和 SVM 模型构建加权 AdaBoost 分类器，实现手手势识别。在十个手势数据集上进行了广泛的实验，并将所提出的算法与三种基准方法进行了比较，以验证其性能。考虑到识别精度和效率，得到了令人鼓舞的结果。
