Emotions taint our life and allow expressing the different facets of the personality. Among the expressions of the human body, facial ones are the most representative of the mindscape of a person. Several works are devoted to it and applications have already been developed. The latter, based on computer vision, are nevertheless facing some limitations and difficulties that are related to the point of view, lighting, occlusions, etc. Artificial Neural Networks (ANN) have been introduced to solve some of these limitations. The latter give satisfactory results, but still have not solved all the problems such as camera angle, the position of the head and, the occlusions, etc. In this paper, we review models of neural networks used in the field of recognition of facial emotions. We also propose an architecture based on the bilinear pooling in order to improve the results obtained by previous works and to provide solutions to solve these recurring constraints. This technique greatly improves the results obtained by architectures based on conventional CNNs.