In this study, the authors propose a novel methodology for static gesture recognition in a complex background using only depth map from Microsoft's Kinect camera. Four different types of features are extracted and analysed on two public static gesture datasets. The features extracted from the segmented hand are geometrical, local binary patterns, number of fingers ( Num ) raised in a gesture and distance of hand palm centre from the fingertips and the valley between the fingers. The hand region is first segmented from the image using depth data followed by the forearm removal. Four multi-class support vector machine (SVM) kernels are also compared and used for recognition of gestures with extracted feature vector as an input. The experimental results achieved recognition accuracy of 99 and $95.7\%$95.7% on two public complex static gesture datasets using Gaussian SVM kernel function as a classifier. The proposed approach is found to be comparable and even outperforms some of the state-of-the-art techniques in terms of high recognition accuracies, even after using a single cue for hand segmentation and extraction of features in the complex background which results in non-dependency on too many cues and much hardware.

中文翻译：

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深度数据和特征描述符融合，用于静态手势识别

在这项研究中，作者提出了一种仅使用Microsoft Kinect相机的深度图在复杂背景下进行静态手势识别的新颖方法。在两个公共静态手势数据集上提取并分析了四种不同类型的特征。从分割的手中提取的特征是几何，局部二进制模式，手指数（ 数词 ）以手势和手掌中心距指尖和手指之间的谷的距离升高。首先使用深度数据从图像中分割出手部区域，然后移除前臂。还比较了四个多类支持向量机（SVM）内核，并将其用于以提取的特征向量作为输入的手势识别。实验结果达到了99的识别精度$ 95.7 \％$95.7％使用高斯SVM核函数作为分类器的两个公共复杂静态手势数据集。发现该方法具有可比性，即使在复杂背景中使用单个提示进行手部分割和特征提取后，即使在复杂背景下使用单个提示进行操作，也无法获得很高的识别准确度，甚至优于某些最新技术。 -依赖过多的提示和硬件。