  We present a new end-to-end network architecture for facial expression
recognition with an attention model. It focuses attention in the human face and
uses a Gaussian space representation for expression recognition. We devise this
architecture based on two fundamental complementary components: (1) facial
image correction and attention and (2) facial expression representation and
classification. The first component uses an encoder-decoder style network and a
convolutional feature extractor that are pixel-wise multiplied to obtain a
feature attention map. The second component is responsible for obtaining an
embedded representation and classification of the facial expression. We propose
a loss function that creates a Gaussian structure on the representation space.
To demonstrate the proposed method, we create two larger and more comprehensive
synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We
compared results with the PreActResNet18 baseline. Our experiments on these
datasets have shown the superiority of our approach in recognizing facial
expressions.
