  Facial Expression Recognition (FER) is a critical task within computer vision
with diverse applications across various domains. Addressing the challenge of
limited FER datasets, which hampers the generalization capability of expression
recognition models, is imperative for enhancing performance. Our paper presents
an innovative approach integrating the MAE-Face self-supervised learning (SSL)
method and multi-view Fusion Attention mechanism for expression classification,
particularly showcased in the 6th Affective Behavior Analysis in-the-wild
(ABAW) competition. By utilizing low-level feature information from the
ipsilateral view (auxiliary view) before learning the high-level feature that
emphasizes the shift in the human facial expression, our work seeks to provide
a straightforward yet innovative way to improve the examined view (main view).
We also suggest easy-to-implement and no-training frameworks aimed at
highlighting key facial features to determine if such features can serve as
guides for the model, focusing on pivotal local elements. The efficacy of this
method is validated by improvements in model performance on the Aff-wild2
dataset, as observed in both training and validation contexts.
