论文标题
语义邻里意识到的深层面部表情识别
Semantic Neighborhood-Aware Deep Facial Expression Recognition
论文作者
论文摘要
与许多其他属性不同，面部表达可以连续变化，因此，输入的语义变化也应导致小规模的输出波动限制。这种一致性很重要。但是，当前的面部表达识别（FER）数据集可能存在极端的不平衡问题，并且缺乏数据和噪声过多，从而阻碍了这种一致性并导致测试时性能降低。在本文中，我们不仅考虑了样本点上的预测准确性，而且还考虑了它们的邻域平滑度，重点是输入的输出稳定性。提出了一种新的方法来制定语义扰动并在训练过程中选择不可靠的样本，从而减少了它们的不良效果。实验显示了所提出的方法的有效性和最新结果的有效性，与最新的方法相比，艾利特网（AffectNet）的上限越来越接近上限，这是迄今为止最大的野外FER数据库。
Different from many other attributes, facial expression can change in a continuous way, and therefore, a slight semantic change of input should also lead to the output fluctuation limited in a small scale. This consistency is important. However, current Facial Expression Recognition (FER) datasets may have the extreme imbalance problem, as well as the lack of data and the excessive amounts of noise, hindering this consistency and leading to a performance decreasing when testing. In this paper, we not only consider the prediction accuracy on sample points, but also take the neighborhood smoothness of them into consideration, focusing on the stability of the output with respect to slight semantic perturbations of the input. A novel method is proposed to formulate semantic perturbation and select unreliable samples during training, reducing the bad effect of them. Experiments show the effectiveness of the proposed method and state-of-the-art results are reported, getting closer to an upper limit than the state-of-the-art methods by a factor of 30\% in AffectNet, the largest in-the-wild FER database by now.
