基于肤色模型与BP神经网络的手势识别

Gesture recognition based on skin color model and BP neural network

针对基于视觉的手势识别率不高,鲁棒性欠佳的问题,提出了一种基于YCb Cr椭圆聚类肤色模型分割手势结合反向传播(BP)神经网络识别的手势识别方法。对采集到的图像序列利用离散余弦变换(DCT)去噪处理和边缘检测,根据人体肤色在YCb Cr空间聚类紧凑的特性提取出手势的形状轮廓,将边缘检测与肤色模型分割结果相与得到分割出的手势,利用加速稳健特性(SURF)算法提取构建手势的特征向量,最后通过BP神经网络对手势图分类和识别。实验结果表明:针对复杂背景下的手势,该算法具有较强的鲁棒性,效率高,识别的准确率可达到96%。

Aiming at the problem of low gesture recognition rate and poor robustness of vision a gesture recognition method based on YCb Cr skin color model of ellipse clustering segmentation gesture and back propagation(BP)neural network recognition is proposed.Firstly,using discrete cosine transform(DCT)for delete noising and edge detection on the captured image sequences.Then extract contour profile of gesture according to the human skin color in the YCb Cr space clustering characteristics,obtain the gestures by combining the results of edge detection with the skin color model segmentation.Extracting feature vectors of gestures by using speeded up robost feature(SURF)algorithm,and finally classify and recognize hand gestures through the BP neural network.The experimental results show that for gestures under complex background the algorithm has strong robustness,high efficiency and the accuracy of recognition can reach to 96%.