| 摘要: |
| 传统手势识别方法需要人工选取特征，选取的特征往往很难适应手势的多变性，从而极大地影响了手势的识别率；提出了一种基于肤色特征和卷积神经网络的手势识别方法；首先采用椭圆肤色模型对复杂背景下的手势样本进行分割，将分割出的手势区域进行二值化和归一化处理，然后构建了一种卷积神经网络对处理过的手势样本进行迭代训练，提取出各类手势关键的高维特征，进而得出手势识别模型；通过该方法训练出的手势模型能够自主地对给定的手势图像进行特征提取和手势分类；实验表明：该手势识别方法在测试集上具有较高的识别率；在现实场景的测试中，该方法也取得了良好的手势识别效果，且实时性和鲁棒性较好。 |
| 关键词: 手势识别 椭圆肤色模型 卷积神经网络 |
| DOI： |
| 分类号: |
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| Gesture Recognition Based on Skin Color Features and Convolutional Neural Network |
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YANG Wen bin， YANG Hui cheng， LU Chun， ZHU Wen bo
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| Abstract: |
| Traditional gesture recognition methods need to select features manually， and the selected features are difficult to adapt to the variability of gestures. So，a gesture recognition method based on skin color feature and convolutional neural network is designed. Firstly， the elliptical model is used to segment the gesture samples in complex background. The obtained gesture regions are binarized and normalized. Then， the processed gesture samples are iteratively trained by convolution neural network. The key high dimensional features of the gestures are extracted， and then the gesture recognition model is obtained. The gesture model trained by this method can autonomously extract feature and classify gesture. Experiments show that the gesture recognition method has high recognition rate on the test set. In the real scene test， the method also achieves good gesture recognition effect and has good real time performance and robustness. |
| Key words: gesture recognition elliptical skin color model convolutional neural network |
