基于曲率局部二值模式的深度图像手势特征提取

针对复杂环境下的深度图像手势特征提取信息冗余量大、编码不稳定等问题，提出了一种改进的基于曲率局部二值模式（LBP）的深度图像手势特征提取算法。该算法首先通过坐标转换将分割出的手势深度数据转换为点云数据；其次利用移动最小二乘法对手势点云数据进行曲面拟合；然后计算出能够更加准确描述物体三维表面几何信息特征的高斯曲率；最后利用改进的LBP均匀模式对高斯曲率数据进行编码形成特征向量。在美国手语（ASL）手势数据库上该算法的平均识别率达到了92.1%，与3D局部二值模式（3DLBP）和梯度LBP相比分别提高了18.5个百分点和13.7个百分点。实验结果表明，该算法可以区分外部轮廓相似但内部结构不同的手势，有效提高了在描述手势深度图像内部细节方面的准确性。

Gesture feature extraction of depth image based on curvature and local binary pattern

Focusing on the information redundancy and encoding instability of depth image for gesture feature extraction in complex environment, an improved gesture feature extraction algorithm for depth image based on curvature-LBP (Local Binary Pattern) was proposed. Firstly, the divided gesture depth data to the point cloud data was converted through the coordinate conversion. Secondly, surface fitting was fulfilled with the moving least square method. And then the Gaussian curvature was calculated to describe the characteristics of the 3D surface geometry more accurately. Finally, the improved LBP uniform model was applied to encode the Gaussian curvature data and form a feature vector. In the American Sign Language (ASL) database, the average recognition rate of the proposed algorithm reached 91.20%, which 18.5 percentage points and 13.7 percentage points higher than 3DLBP and gradient LBP. Simulation results show that the proposed algorithm can recognize the gestures with similar outline and different shape, and improve the precision of describing the internal details in gesture depth image.