重庆工商大学学报（自然科学版）2024，Vol.41Issue(1)：21-29,9.DOI:10.16055/j.issn.1672-058X.2024.0001.003
改进残差网络与峰值帧的微表情识别
Micro-expression Recognition Based on Improved Residual Network and Apex Frame
摘要
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.关键词
微表情识别/残差网络/峰值帧/深度学习Key words
micro-expression recognition/residual network/apex frame/deep learning分类
化学化工引用本文复制引用
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].重庆工商大学学报（自然科学版）,2024,41(1):21-29,9.基金项目
安徽省自然科学基金项目(2108085MF213) （2108085MF213）
安徽省高校自然科学研究项目(KJ2021A0516) （KJ2021A0516）
国家自然科学基金面上项目(61976005) （61976005）
国家级大学生创新创业项目(202110363102,202210363094). （202110363102,202210363094）
