[1]闫河,李梦雪,张宇宁,等.面向表情识别的重影非对称残差注意力网络模型[J].智能系统学报,2023,18(2):333-340.[doi:10.11992/tis.202201003]
YAN He,LI Mengxue,ZHANG Yuning,et al.A ghost asymmetric residual attention network model for facial expression recognition[J].CAAI Transactions on Intelligent Systems,2023,18(2):333-340.[doi:10.11992/tis.202201003]
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YAN He,LI Mengxue,ZHANG Yuning,et al.A ghost asymmetric residual attention network model for facial expression recognition[J].CAAI Transactions on Intelligent Systems,2023,18(2):333-340.[doi:10.11992/tis.202201003]
面向表情识别的重影非对称残差注意力网络模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第2期
页码:
333-340
栏目:
学术论文—机器感知与模式识别
出版日期:
2023-05-05
- Title:
- A ghost asymmetric residual attention network model for facial expression recognition
- Keywords:
- expression recognition; feature extraction; ResNet50; Ghost module; Mish; asymmetric residual attention; depthwise separable convolution; deep learning
- 分类号:
- TP391
- 摘要:
- 针对ResNet50中的Bottleneck经过1×1卷积降维后主干分支丢失部分特征信息而导致在表情识别中准确率不高的问题，本文通过引入Ghost模块和深度可分离卷积分别替换Bottleneck中的1×1卷积和3×3卷积，保留更多原始特征信息，提升主干分支的特征提取能力；利用Mish激活函数替换Bottleneck中的ReLU激活函数，提高了表情识别的准确率；在此基础上，通过在改进的Bottleneck之间添加非对称残差注意力模块(asymmetric residual attention block, ARABlock)来提升模型对重要信息的表示能力，从而提出一种面向表情识别的重影非对称残差注意力网络（ghost asymmetric residual attention network, GARAN）模型。对比实验结果表明，本文方法在FER2013和CK+表情数据集上具有较高的识别准确率。
- Abstract:
- In this paper, a solution is proposed to address the low accuracy in facial expression recognition that results from the 1×1 convolution dimensionality reduction of the Bottleneck in ResNet50. To do so, the authors introduce the Ghost module and depth separable convolution to replace the 1×1 and 3×3 convolutions in the Bottleneck, respectively, in order to preserve more of the original feature information and improve the feature extraction ability of the trunk branch. The Mish activation function is also used to replace the ReLU activation function in the Bottleneck, further enhancing the accuracy of facial expression recognition. To further improve the ability of the model to express important information, the authors also introduce an asymmetric residual attention block (ARABlock) between the improved Bottlenecks. The proposed method, which is referred to as the ghost asymmetric residual attention network (GARAN) model, shows high recognition accuracy on the FER2013 and CK+ facial expression datasets based on comparative experimental results.
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备注/Memo
收稿日期:2022-01-04。
基金项目:国家重点研发计划“智能机器人”重点专项(2018YFB1308602)；国家自然科学基金面上项目(61173184)；重庆市自然科学基金项目(cstc2018jcyjAX0694).
作者简介:闫河,教授,主要研究方向为小波分析、目标跟踪、计算机视觉与视觉测量。发表学术论文90余篇;李梦雪,硕士研究生,主要研究方向为计算机视觉、情感感知;张宇宁,硕士研究生,主要研究方向为计算机视觉、语音处理
通讯作者:闫河. E-mail：cqyanhe@163.com
更新日期/Last Update:
1900-01-01