论文标题
个性化面部表情识别的姿势感知的对抗领域适应
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition
论文作者
论文摘要
当前的面部表达识别方法无法同时应对姿势和主体变化。 在本文中，我们提出了一种新型的无监督的对抗领域适应方法，可以同时减轻这两种变化。特别是，我们的方法包括三种学习策略：对抗领域的适应性学习，交叉对抗特征学习和重建学习。第一个旨在在源域中学习与姿势和表达相关的特征表示形式，并通过施加对抗性学习来调整目标域的特征分布。通过使用个性化的对抗领域的适应，该学习策略可以减轻主题的变化并从源域中利用信息，以帮助目标域中的学习。 第二种是通过冲动与姿势相关的特征表示表达式不融为一体，并且与表达相关的特征表示姿势表示姿势无构姿势姿势无关。 最后一个可以通过应用面部图像重建来进一步提高特征学习，从而使学习的表达相关特征表示形式更具姿势和身份。 四个基准数据集的实验结果证明了该方法的有效性。
Current facial expression recognition methods fail to simultaneously cope with pose and subject variations. In this paper, we propose a novel unsupervised adversarial domain adaptation method which can alleviate both variations at the same time. Specially, our method consists of three learning strategies: adversarial domain adaptation learning, cross adversarial feature learning, and reconstruction learning. The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning. By using personalized adversarial domain adaptation, this learning strategy can alleviate subject variations and exploit information from the source domain to help learning in the target domain. The second serves to perform feature disentanglement between pose- and expression-related feature representations by impulsing pose-related feature representations expression-undistinguished and the expression-related feature representations pose-undistinguished. The last can further boost feature learning by applying face image reconstructions so that the learned expression-related feature representations are more pose- and identity-robust. Experimental results on four benchmark datasets demonstrate the effectiveness of the proposed method.
