  Facial Expression Recognition (FER) in the wild is extremely challenging due
to occlusions, variant head poses, face deformation and motion blur under
unconstrained conditions. Although substantial progresses have been made in
automatic FER in the past few decades, previous studies are mainly designed for
lab-controlled FER. Real-world occlusions, variant head poses and other issues
definitely increase the difficulty of FER on account of these
information-deficient regions and complex backgrounds. Different from previous
pure CNNs based methods, we argue that it is feasible and practical to
translate facial images into sequences of visual words and perform expression
recognition from a global perspective. Therefore, we propose Convolutional
Visual Transformers to tackle FER in the wild by two main steps. First, we
propose an attentional selective fusion (ASF) for leveraging the feature maps
generated by two-branch CNNs. The ASF captures discriminative information by
fusing multiple features with global-local attention. The fused feature maps
are then flattened and projected into sequences of visual words. Second,
inspired by the success of Transformers in natural language processing, we
propose to model relationships between these visual words with global
self-attention. The proposed method are evaluated on three public in-the-wild
facial expression datasets (RAF-DB, FERPlus and AffectNet). Under the same
settings, extensive experiments demonstrate that our method shows superior
performance over other methods, setting new state of the art on RAF-DB with
88.14%, FERPlus with 88.81% and AffectNet with 61.85%. We also conduct
cross-dataset evaluation on CK+ show the generalization capability of the
proposed method.
