In this paper, an approach for Facial Expressions Recognition (FER) based on a multi-facial patches (MFP) aggregation network is proposed. Deep features are learned from facial patches using convolutional neural sub-networks and aggregated within one architecture for expression classification. Besides, a framework based on two data augmentation techniques is proposed to expand FER labels training datasets. Consequently, the proposed shallow convolutional neural networks (CNN) based approach does not need large datasets for training. The proposed framework is evaluated on three FER datasets. Results show that the proposed approach achieves state-of-art FER deep learning approaches performance when the model is trained and tested on images from the same dataset. Moreover, the proposed data augmentation techniques improve the expression recognition rate, and thus can be a solution for training deep learning FER models using small datasets. The accuracy degrades significantly when testing for dataset bias. A fine-tuning can overcome the problem of transition from laboratory-controlled conditions to in-the-wild conditions. Finally, the emotional face is mapped using the MFP-CNN and the contribution of the different facial areas in displaying emotion as well as their importance in the recognition of each facial expression are studied.

本文提出了一种基于多面部补丁（MFP）聚合网络的面部表情识别（FER）方法。通过使用卷积神经子网从面部补丁学习深度特征，并将其聚集在一种体系中以进行表达分类。此外，提出了一种基于两种数据扩充技术的框架来扩展FER标签训练数据集。因此，提出的基于浅层卷积神经网络（CNN）的方法不需要用于训练的大型数据集。在三个FER数据集上对提出的框架进行了评估。结果表明，在对来自同一数据集的图像进行训练和测试时，该方法可达到最新的FER深度学习方法性能。此外，提出的数据增强技术提高了表达识别率，因此可以成为使用小数据集训练深度学习FER模型的解决方案。测试数据集偏差时，准确性会大大降低。微调可以克服从实验室控制条件到野外条件转换的问题。最后，使用MFP-CNN绘制情感面孔，并研究不同面部区域在展示情感中的作用以及它们在识别每个面部表情中的重要性。