基于传统的手部轮廓特征提取不能应对飞行模拟环境下的脸部肤色、遮挡、光照影响, 以及传统的傅里叶描述子特征容易受到背景、手的姿态变化, 且对手势描述能力有限等问题, 对传统的手部分割和特征提取方法改进. 本文首先对采集的数据集进行肤色处理, 然后结合调用的手部关键点模型检测出手部22个特征点, 采用八向种子填充算法进行图像分割. 接着对手部轮廓和关键点连接骨架进行傅里叶描述子算法特征提取, 最后通过支持向量机算法对提取的手势特征数据集进行训练、识别. 实验结果表明, 本文方法具有较好的手部分割, 特征提取不易受到背景、手的姿态变化的影响, 能够很好地应对在飞行模拟环境下的复杂背景下的干扰, 识别准确率能够达到98%. 本文研究在传统的手势识别算法中有一定的提高作用, 在手部交互技术领域有很重要的应用价值.
The traditional hand contour feature extraction can not deal with the effect of facial skin color, occlusion, lighting in the flight simulation environment. The traditional Fourier descriptor features are susceptible to background and hand posture changes, and have limited ability to describe gestures, etc. Hence, this study proposes a methed to improve the traditional hand segmentation and feature extraction methods. Firstly, skin color processing is performed on the collected data set, and then the 22 key points of the hand are detected in combination with the called hand key point model, and an eight-way seed filling algorithm is used for image segmentation. Then, the contours of the hand and key points are connected to the skeleton to extract the features of the Fourier descriptor algorithm. Finally, the Support Vector Machine (SVM) algorithm is used to train and recognize the extracted gesture feature data set. The experimental results show that the method in this study has good hand segmentation, feature extraction is not easily affected by changes in the background and hand posture. Hence, it can well cope with interference in a complex background in a flight simulation environment, with the recognition accuracy reaching 98%. The research presented in this paper has a certain role in improving the traditional gesture recognition algorithm and has very important application value in the field of hand interaction technology.