FERAtt: Facial Expression Recognition With Attention Net

Pedro D. Marrero Fernandez, Fidel A. Guerrero Pena, Tsang Ing Ren, Alexandre Cunha; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Fernandez_2019_CVPR_Workshops,
author = {Marrero Fernandez, Pedro D. and Guerrero Pena, Fidel A. and Ing Ren, Tsang and Cunha, Alexandre},
title = {FERAtt: Facial Expression Recognition With Attention Net},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}