结合自注意力特征过滤分类器和双分支GAN的面部表情识别

Facial Expression Recognition Combining Self-Attention Feature Filtering Classifier and Two-Branch GAN

现有面部表情识别方法提取表情特征时通常容易与其它面部属性混合,不利于面部表情的识别.对此,文中提出结合自注意力特征过滤分类器和双分支生成对抗网络的面部表情识别方法.首先,使用双分支生成对抗网络学习辨别性的表情表示,提出自注意力特征过滤分类器作为表情的分类模块.使用级联的LayerNorm和ReLU将低激活单元归零并保留高激活单元,生成多级特征.使用自注意力融合输出多级特征的预测结果,在一定程度上消除噪声对识别结果的影响.然后,提出基于滑动模块的双重图像一致性损失监督模型,学习具有辨别性的表情表示,使用滑动窗口计算重构损失,关注细节信息.最后,在CK+、RAF-DB、TFEID、BAUM-2i数据集上的实验表明文中方法识别效果较优.

The expression features extracted by the existing facial expression recognition methods are usually mixed with other facial attributes,which is not conducive to facial expression recognition.A facial expression recognition model combining self-attention feature filter classifier and two-branch generative adversarial network is proposed.Two-branch generative adversarial network is introduced to learn discriminative expression representation,and a self-attention feature filtering classifier is proposed as the expression classification module.The cascaded LayerNorm and ReLU are employed to zero the low activation unit and retain the high activation unit to generate multi-level features.The self-attention is utilized to fuse and output the prediction results of multi-level features,and consequently the influence of noise on the recognition results is eliminated to a certain extent.A sliding module based dual image consistency loss supervised model is proposed to learn discriminative expression representations.The reconstruction loss is calculated by a sliding window and more attention is paid to the details.Finally,experiments on CK+,RAF-DB,TFEID and BAUM-2i datasets show the proposed model achieves better recognition results.