非平衡样本下面部情感多类别图像分类的纠错
Error correction in multiclass image classification of facial emotion on unbalanced samples
AI总结：
针对非平衡样本下面部情感多分类的纠错问题，提出带注意力机制的LSTM模型，通过六类训练加第七类纠错的实验验证了方法有效性，可提升小类指标，适用于偏斜分布分类及稀有事件搜索场景。
摘要：
本文研究非平衡样本下面部图像多类别分类的纠错问题。本研究基于对一个数据帧的分析，该数据帧包含标注了不同年龄人群七种不同情绪状态的图像。研究特别关注类别不平衡问题，即部分情绪类别的样本数量显著多于其他类别。为解决该分类问题，本文采用了一种基于LSTM（长短期记忆网络）的神经网络模型，该模型带有注意力机制，可聚焦于对情感识别具有信息价值的面部关键区域。实验部分，模型在所有可能的六类子集配置上进行训练，随后对训练阶段排除的第七类进行纠错。结果表明，所有类别均可实现纠错，尽管成功程度存在差异：部分类别恢复效果较好，部分则较差。此外，在测试样本上，对部分类别进行纠错时，观测到小类别的关键质量指标有所提升，这表明所提方法在解决与稀有事件搜索相关的应用问题（例如反欺诈系统）中具有应用前景。因此，所提方法可有效应用于面部表情分析系统，以及类别分布偏斜下需要稳定分类的任务中。
英文摘要：
This paper considers the problem of error correction in multi-class classification of face images on unbalanced samples. The study is based on the analysis of a data frame containing images labeled by seven different emotional states of people of different ages. Particular attention is paid to the problem of class imbalance, in which some emotions significantly prevail over others. To solve the classification problem, a neural network model based on LSTM with an attention mechanism focusing on key areas of the face that are informative for emotion recognition is used. As part of the experiments, the model is trained on all possible configurations of subsets of six classes with subsequent error correction for the seventh class, excluded at the training stage. The results show that correction is possible for all classes, although the degree of success varies: some classes are better restored, others are worse. In addition, on the test sample, when correcting some classes, an increase in key quality metrics for small classes was recorded, which indicates the promise of the proposed approach in solving applied problems related to the search for rare events, for example, in anti-fraud systems. Thus, the proposed method can be effectively applied in facial expression analysis systems and in tasks requiring stable classification under skewed class distribution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
