基于特征迭代选择的跨库微表情识别方法
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1.河南理工大学物理与电子信息学院;2.许继电气股份有限公司

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TP391

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河南省科技攻关项目(212102210504)


Cross-Database Micro-Expression Recognition Method Based on Feature Iterative Selection
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1.School of Physics and Electronic Information, Henan Polytechnic University;2.XuJi Electric CO., Ltd

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    摘要:

    针对跨库微表情识别中因训练和测试样本特征分布不一致而造成识别效果不理想的问题,提出一种基于特征迭代选择(Feature Iteration Selection, FIS)的跨库微表情识别的领o域泛化方法。FIS迭代地摒弃在训练阶段对模型分类结果影响较大的特征,强迫网络激活剩余特征参与训练,防止全连接层用最具有预测性的特征子集进行预测,平衡模型从不同特征中提取信息的强度,从而提高模型的泛化能力。通过在三个广泛使用的微表情数据集上的实验表明,FIS方法的平均准确率为54.54%,平均F1值为54.20%,优于主流的领域自适应和领域泛化方法,验证了所提出的FIS方法在跨库微表情识别任务上的优越性。

    Abstract:

    To address the problem of unsatisfactory recognition results in cross-database micro-expression recognition due to inconsistent distribution of features between training and testing samples, a domain generalization method for cross-database micro-expression recognition based on Feature Iteration Selection (FIS) is proposed. FIS iteratively discards the features that have a large impact on the classification results of the model in the training phase, forcing the network to activate the remaining features to participate in training, preventing the fully connected layer from predicting with the most predictive subset of features, and balancing the strength of the model in extracting information from different features, thus improving the generalization ability of the model . Experiments on three widely used micro-expression datasets show that the FIS method achieves an average accuracy of 54.54% and an average F1 value of 54.20%, outperforming the mainstream domain adaptive and domain generalization methods, validating the superiority of the proposed FIS method for cross-database micro-expression recognition tasks.

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张延良,尹梦涛,杨亚璞. 基于特征迭代选择的跨库微表情识别方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-08-21
  • 最后修改日期:2024-02-01
  • 录用日期:2024-02-20
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