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We introduced MV-FER, a novel multi view facial expression recognition framework that fundamentally differs from existing approaches in its systematic integration of domain-specific knowledge with multi-view learning paradigms.
Instead of consuming AU heatmaps as inputs, we recast them as structured supervisory signals that align network attention with expression-discriminative regions, while a dual-branch architecture processes each image and its horizontal flip to enforce flip-semantic consistency.
Empirically, MV-FER achieves 89.54% accuracy on RAF-DB, surpassing recent strong published works that employ the same ResNet backbone, thereby demonstrating that performance gains arise from our multi-view modeling approach rather than architectural advantages.
Future work includes the development of self-supervised cross-view alignment mechanisms that could eliminate landmark dependencies while preserving the benefits of structured anatomical guidance. |
IEEE/ICACT20260151 Slide.17
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