Facial expression recognition (FER) in the wild is a very challenging problem due to different expressions under complex scenario (e.g., large head pose, illumination variation, occlusions, etc.), leading to suboptimal FER performance. Accuracy in FER heavily relies on discovering superior discriminative, emotion‐related features. In this paper, we propose an end‐to‐end module to disentangle latent emotion discriminative factors from the complex factors variables for FER to obtain salient emotion features. The training of proposed method contains two stages. First of all, emotion samples are used to obtain the latent representation using a variational auto‐encoder with reconstruction penalization. Furthermore, the latent representation as the input is thrown into a disentangling layer to learn a set of discriminative emotion factors through the attention mechanism (e.g., a Squeeze‐and‐Excitation block) that encourages to separate emotion‐related factors and nonaffective factors. Experimental results on public benchmark databases (RAF‐DB and FER2013) show that our approach has remarkable performance in complex scenes than current state‐of‐the‐art methods.

中文翻译：

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学习解开情感因素以在野外进行面部表情识别

野外的面部表情识别（FER）是一个非常具有挑战性的问题，因为复杂情况下（例如，较大的头部姿势，光照变化，遮挡等）下的不同表情会导致FER性能欠佳。FER的准确性在很大程度上取决于发现与情感相关的卓越判别功能。在本文中，我们提出了一个端到端模块，以从FER的复杂因子变量中分解出潜在的情感判别因子，以获得显着的情感特征。所提出方法的训练包括两个阶段。首先，使用带有重构惩罚的变分自动编码器将情感样本用于获得潜在表示。此外，作为输入的潜在表示被扔到一个纠缠层中，以通过注意力机制（例如，挤压和激发块）来学习一组区分性情感因素，该机制鼓励将情感相关因素和非情感因素分开。在公共基准数据库（RAF-DB和FER2013）上的实验结果表明，与当前最新方法相比，我们的方法在复杂场景中具有出色的性能。