FERAtt: Facial Expression Recognition with Attention Net

Understand

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.