FERAtt: Facial Expression Recognition with Attention Net [article]

Pedro D. Marrero Fernandez, Fidel A. Guerrero Peña, Tsang Ing Ren, Alexandre Cunha
2019 arXiv   pre-print
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
more » ... re 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.
arXiv:1902.03284v1 fatcat:r4f5pkiq4je6bgvzcb24nj5lzq