| 嵌入注意力机制的多尺度深度可分离表情识别 |
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| 引用本文： | 宋玉琴,高师杰,曾贺东,熊高强.嵌入注意力机制的多尺度深度可分离表情识别[J].北京航空航天大学学报,2022,48(12):2381-2387. |
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| 作者姓名： | 宋玉琴 高师杰 曾贺东 熊高强 |
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| 作者单位： | 西安工程大学 电子信息学院, 西安 710600 |
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| 基金项目： | 中国纺织工业联合会科技性指导项目2019062 |
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| 摘 要： | 针对面部表情识别中，传统机器学习方法特征提取较为复杂，浅层卷积神经网络识别率不高，以及深度卷积神经网络易带来梯度爆炸或弥散的问题，构建了残差网络嵌入注意力机制的多尺度深度可分离表情识别网络。通过多层多尺度深度可分离残差单元的叠加进行不同尺度的表情特征提取，使用CBAM注意力机制进行表情特征的筛选，提升有效表情特征权重的表达，削弱训练数据的噪声影响。所提网络模型在Fer-2103和CK+表情数据集分别取得了73.89%和97.47%的准确度，表明所提网络具有较强的泛化性。
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| 关 键 词： | 表情识别 注意力机制 多尺度特征提取 深度可分离卷积 残差网络 |
| 收稿时间： | 2021-03-10 |
Multi-scale depthwise separable convolution facial expression recognition embedded in attention mechanism |
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| Affiliation: | School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710600, China |
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| Abstract: | For facial expression recognition, traditional machine learning method features extraction is relatively complex, shallow convolutional neural network recognition rate is not high, and deep convolutional network is easy to cause gradient explosion or dispersion problems. This paper constructs the multi-scale deep separable expression recognition network with residual network which embedded in attention mechanism. Through superposition of multi-layer and multi-scale depth separable residual elements, facial expression feature extraction of different scales is achieved; in the meanwhile, CBAM attention mechanism was used to screen the expression features for the purpose of improving the expression of the weight of the expression features and weakening the noise impact of training data. The algorithm network model in this paper achieves accuracy of 73.89% and 97.47% in Fer-2103 and CK+ expression data sets respectively, which indicates that this network has strong generalization. |
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