| 引用本文: | 任 宇, 陈新泉, 王岱嵘, 陈新怡.改进残差网络与峰值帧的微表情识别(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2024，41（1）：21-29 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 微表情(Micro Expression, ME)是人们流露内心情感时展现出的细微面部表情。 针对微表情识别的样
本较少且不同类别数量分布不均导致难以识别和识别准确率较低的问题,提出能够提高微表情识别准确率的模型
框架。 方法 提取微表情视频序列中含有更多关键表情信息的峰值帧;使用加入 SE 模块的改进残差网络 SEResNeXt-50 对微表情的峰值帧进行特征提取,其中 SE 模块可以更好地学习特征中的关键信息,ResNeXt 通过分组
卷积的方式用稀疏结构取代密集结构从而使结构更加简化,提升了识别效率。 与此同时,使用 Focal Loss 损失函数
可以更好地解决因微表情数据的不平衡带来的模型性能问题。 结果 在微表情数据集 CASMEⅡ上进行了仿真实
验,可以发现改进的残差网络与峰值帧提高了微表情识别的准确率与 F1 值。 结论 改进的残差网络与峰值帧可以
降低数据集较少所带来的影响,使模型有着良好的拟合效果,同时改善了在不同类别上表现差异较大的问题,提升
了微表情的识别准确率,对于微表情识别有着更好的识别性能。 |
| 关键词: 微表情识别 残差网络 峰值帧 深度学习 |
| DOI： |
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| Micro-expression Recognition Based on Improved Residual Network and Apex Frame |
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REN Yu, CHEN Xinquan, WANG Dairong, CHEN Xinyi
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School of Computer and Information, Anhui Polytechnic University, Anhui Wuhu 241000, China
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| Abstract: |
| Objective Micro-expression ME is the subtle facial expression that reveals one ?? s inner emotions. The
number of samples for micro-expression recognition is small and the number of different categories is uneven leading to
difficulty in recognition and low recognition accuracy. In view of this a model framework that can improve the accuracy of
micro-expression recognition was proposed. Methods Peak frames containing more key expression information were
extracted from the micro-expression video sequences. An improved residual network SE-ResNeXt-50 incorporating the
SE module was used to extract features from the apex frames of micro-expressions. The SE module learned the key
information in the features better. ResNeXt simplified the structure by replacing the dense structure with a sparse one by
means of group convolution thus improving the recognition efficiency. At the same time the Focal Loss function was used
to better solve the model performance problems caused by the imbalance of micro-expression data. Results Simulation
experiments were conducted on the micro-expression dataset CASME II and it was found that the improved residual
network and apex frames improved the accuracy and F1 value of micro-expression recognition. Conclusion The improved
residual network and apex frames can reduce the impact caused by fewer data sets so that the model has a good fitting
effect. At the same time it can mitigate the impact caused by the performance differences in different categories improve the accuracy of micro-expression recognition and have better recognition performance for micro-expression recognition. |
| Key words: micro-expression recognition residual network apex frame deep learning |
