当前位置: X-MOL 学术Intell. Data Anal. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Efficient facial expression recognition based on convolutional neural network
Intelligent Data Analysis ( IF 1.7 ) Pub Date : 2021-01-26 , DOI: 10.3233/ida-194965
Yongxiang Cai 1, 2 , Jingwen Gao 1 , Gen Zhang 1 , Yuangang Liu 1
Affiliation  

The goal of research in Facial Expression Recognition (FER) is to build a robust and strong recognizability model. In this paper, we propose a new scheme for FER systems based on convolutional neural network. Part of the regular convolution operation is replaced by depthwise separable convolution to reduce the number of parameters and the computational workload; the self-adaption joint loss function is adopted to improve the classification performance. In addition, we balance our train set through data augmentation, and we preprocess the input images through illumination processing, face detection, and other methods, effectively maximizing the expression recognition rate. Experiments to validate our methods are conducted based on the TensorFlow platform and Fer2013 dataset. We analyze the experimental results before and after train set balancing and network model modification, and we compare our results with those of other researchers. The results show that our method is effective at increasing the expression recognition rate under the same experiment conditions. We further conduct an experiment on our own expression dataset relevant to driving safety, and it yields similar results.

中文翻译:

基于卷积神经网络的高效表情识别

面部表情识别(FER)的研究目标是建立一个强大而强大的可识别性模型。在本文中,我们提出了一种基于卷积神经网络的FER系统新方案。常规卷积运算的一部分被深度可分离卷积代替,以减少参数的数量和计算量;采用自适应联合损失函数提高分类性能。此外,我们通过数据增强来平衡训练集,并通过照明处理,面部检测等方法对输入图像进行预处理,从而有效地最大化表情识别率。基于TensorFlow平台和Fer2013数据集进行了验证我们方法的实验。我们分析了列车平衡和网络模型修改前后的实验结果,并将我们的结果与其他研究人员进行了比较。结果表明,在相同的实验条件下,我们的方法可以有效提高表达识别率。我们进一步在自己的与驾驶安全性相关的表达数据集上进行了实验,得出的结果相似。
更新日期:2021-02-03
down
wechat
bug