基于卷积神经网络的矿工面部表情识别方法

Miners' facial expression recognition method based on convolutional neural network

针对传统的矿工面部表情识别方法识别率较低、算法复杂等问题,以卷积神经网络为基础,结合支持向量机算法中的非线性映射函数,提出了基于卷积神经网络的矿工面部表情识别方法。卷积神经网络采用权值共享的策略,运用固定权值直接构造卷积层,并依照匹配生长规则确定网络层次结构。将经过预处理的矿工面部表情图像作为卷积神经网络的测试集和训练集,使用支持向量机对表征矿工面部表情特征的神经元进行分类,从而实现对矿工面部表情的分类识别。实验结果表明,该方法对矿工面部表情的识别率达到90.71%,能够满足实际应用需要。

In view of problems of low recognition rate and complex algorithm of traditional miner's facial expression recognition methods,based on convolutional neural network and combining with nonlinear mapping function in support vector machine algorithm,a miners'facial expression recognition method based on convolutional neural network was proposed.The convolutional neural network adopts sharing weights strategy,constructs convolutional layer directly with fixed weights,and determine network hierarchy according to matching growth rules.Preprocessed miner's facial expression images are used as test set and training sets of the convolutional neural network.Supportive vector machine is used to classify neurons that represent miner's facial expression features,so as to realize classification and recognition of miner's facial expressions.The experimental results show that the recognition rate of miner's facial expression of the proposed method reaches 90.71%,which can meet the practical application needs.