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Disentangling Identity and Pose for Facial Expression Recognition
IEEE Transactions on Affective Computing ( IF 11.2 ) Pub Date : 2022-08-10 , DOI: 10.1109/taffc.2022.3197761
Jing Jiang 1 , Weihong Deng 1
Affiliation  

Facial expression recognition (FER) is a challenging problem because the expression component is always entangled with other irrelevant factors, such as identity and head pose. In this work, we propose an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature representation. We regard the holistic facial representation as the combination of identity, pose and expression. These three components are encoded with different encoders. For identity encoder, a well pre-trained face recognition model is utilized and fixed during training, which alleviates the restriction on specific expression training data in previous works and makes the disentanglement practicable on in-the-wild datasets. At the same time, the pose and expression encoder are optimized with corresponding labels. Combining identity and pose feature, a neutral face of input individual should be generated by the decoder. When expression feature is added, the input image should be reconstructed. By comparing the difference between synthesized neutral and expressional images of the same individual, the expression component is further disentangled from identity and pose. Experimental results verify the effectiveness of our method on both lab-controlled and in-the-wild databases and we achieve state-of-the-art recognition performance.

中文翻译:

为面部表情识别解开身份和姿势

面部表情识别 (FER) 是一个具有挑战性的问题,因为表情成分总是与其他不相关的因素纠缠在一起,例如身份和头部姿势。在这项工作中,我们提出了一种身份和姿势分离式面部表情识别 (IPD-FER) 模型,以学习更具辨别力的特征表示。我们将整体面部表征视为身份、姿势和表情的组合。这三个组件使用不同的编码器进行编码。对于身份编码器,在训练过程中利用并固定了一个经过良好预训练的人脸识别模型,这减轻了以往工作对特定表情训练数据的限制,使解耦在野外数据集上变得可行。同时,使用相应的标签优化姿势和表情编码器。结合身份和姿势特征,解码器应生成输入个体的中性面孔。添加表情特征后,需要重构输入图像。通过比较同一个人的合成中性和表情图像之间的差异,表情成分进一步从身份和姿势中解脱出来。实验结果验证了我们的方法在实验室控制和野外数据库上的有效性,并且我们实现了最先进的识别性能。表情成分进一步从身份和姿势中分离出来。实验结果验证了我们的方法在实验室控制和野外数据库上的有效性,并且我们实现了最先进的识别性能。表情成分进一步从身份和姿势中分离出来。实验结果验证了我们的方法在实验室控制和野外数据库上的有效性,并且我们实现了最先进的识别性能。
更新日期:2022-08-10
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