  Facial Expressions Recognition(FER) on low-resolution images is necessary for
applications like group expression recognition in crowd scenarios(station,
classroom etc.). Classifying a small size facial image into the right
expression category is still a challenging task. The main cause of this problem
is the loss of discriminative feature due to reduced resolution.
Super-resolution method is often used to enhance low-resolution images, but the
performance on FER task is limited when on images of very low resolution. In
this work, inspired by feature super-resolution methods for object detection,
we proposed a novel generative adversary network-based feature level
super-resolution method for robust facial expression recognition(FSR-FER). In
particular, a pre-trained FER model was employed as feature extractor, and a
generator network G and a discriminator network D are trained with features
extracted from images of low resolution and original high resolution. Generator
network G tries to transform features of low-resolution images to more
discriminative ones by making them closer to the ones of corresponding
high-resolution images. For better classification performance, we also proposed
an effective classification-aware loss re-weighting strategy based on the
classification probability calculated by a fixed FER model to make our model
focus more on samples that are easily misclassified. Experiment results on
Real-World Affective Faces (RAF) Database demonstrate that our method achieves
satisfying results on various down-sample factors with a single model and has
better performance on low-resolution images compared with methods using image
super-resolution and expression recognition separately.
