FERAtt: Facial Expression Recognition With Attention Net
Abstract
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.
Additional Information
© 2019 IEEE. The authors thanks the financial support from the Brazilian funding agency FACEPE and CETENE for usage of the computational facility.Additional details
- Eprint ID
- 102609
- Resolver ID
- CaltechAUTHORS:20200417-134029548
- Fundação do Amparo a Ciência e Tecnologia (FACEPE)
- Centro de Tecnologias Estratégicas do Nordeste (CETENE)
- Created
-
2020-04-17Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field