The face expression is the first thing we pay attention to when we want to understand a person's state of mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research field. In this paper, because the small size of available training datasets, we propose a novel data augmentation technique that improves the performances in the recognition task. We apply geometrical transformations and build from scratch GAN models able to generate new synthetic images for each emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with different architectures. To measure the generalization ability of the models, we apply extra-database protocol approach, namely we train models on the augmented versions of training dataset and test them on two different databases. The combination of these techniques allows to reach average accuracy values of the order of 85\% for the InceptionResNetV2 model.

翻译：面部表情是我们理解他人心理状态时首先关注的特征。因此，实现面部表情的自动识别是一个极具价值的研究领域。针对现有训练数据集规模较小的问题，本文提出一种新颖的数据增强技术，可有效提升识别任务的性能。我们通过几何变换并构建能够为每种情绪类型生成新合成图像的对抗生成网络模型，在此基础上对增强后的数据集进行不同架构的预训练卷积神经网络微调。为衡量模型泛化能力，采用跨数据库协议方法——即在增强后的训练数据集上训练模型，并在两个不同数据库上进行测试。实验表明，这些技术的组合使InceptionResNetV2模型达到了约85%的平均准确率。