A practical method called Dynamic Resolution Guidance for Facial Expression Recognition (DRGFER) that effectively recognizes facial expressions in images with varying resolutions without compromising FER model accuracy.
Introducing LANMSFF, a lightweight attention-based deep network incorporating multi-scale feature fusion, to address challenges in facial expression recognition.
Ensemble learning methods improve compound expression recognition by combining local and global features from different models.
Proposing a zero-shot approach for recognizing compound expressions using a visual language model integrated with CNN networks.
Proposing a novel approach, FACE-BE-SELF, for classifying adult and child facial expressions through deep domain adaptation and feature fusion.
The author introduces the A3lign-DFER method to address challenges in dynamic facial expression recognition, enhancing CLIP's suitability for the task through comprehensive alignment in affective, dynamic, and bidirectional aspects.
The author proposes a local non-local joint network to adaptively enhance facial crucial regions in feature learning for facial expression recognition.