Hand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

手部分割是手部姿势识别的第一步。为了减少手部分割步骤中可变照明的影响，提出了一种新的CbCr-I分量高斯混合模型（GMM）来检测皮肤区域。使用皮肤检测技术，基于提出的CbCr-1成分GMM和新的自适应阈值，从图像中选择手部区域作为关注区域。提出了一种新的极坐标描述的手形分布特征，以提取手形轮廓特征，以解决某些基于形状的方法中的错误识别问题，并在不同手势具有相同数量的伸出手指的情况下有效地识别手势。利用多类支持向量机分类器来识别手部姿势。在我们的数据集上进行了实验，以验证该方法的可行性。结果表明，与其他方法相比，该方法是有效的。