基于VGG16网络的人脸情绪识别

Facial emotion recognition based on VGG16 network

近年来,如何通过人工智能对人的面部表情进行识别分析成为一个研究热点,利用人工智能可以快速地分析人的面部情绪,并以此为基础进行进一步研究。在深度学习中,传统的卷积神经网络存在对面部表情特征的提取不充分以及计算机参数量较大的问题,导致分类准确率较低。因此,提出了基于VGG16网络的人脸表情识别算法,通过与InceptionV3、InceptionResNetV2、ResNet50等模型实验对比,结果表明,VGG16神经网络在FER2013PLUS测试数据集上的识别准确率为79%,准确率比传统的卷积神经网络高。

In recent years,how to recognize and analyze people’s facial expressions through artificial intelligence has become a research hotspot.Using artificial intelligence can quickly analyze people’s facial emotions,and further research is carried out on this basis.In deep learning,the traditional convolutional neural network can not extract facial expression features sufficiently,and the amount of computer parameters is large,which leads to low classification accuracy.Therefore,a facial expression recognition algorithm based on VGG16 neural network is proposed.Compared with the model experiments of InceptionV3,InceptionResNetV2 and ResNet50,the results show that the recognition accuracy of VGG16 neural network on FER2013 PLUS test data set is 79%,which is higher than that of traditional convolution neural network.