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We benchmark MV-FER against state-of-the-art methods on the RAF-DB dataset using the evaluation setup described above.
MV-FER is assessed in three variants, demonstrating progressive improvements through the integration of augmented views and AU-guided heatmap views. The quantitative results are reported in Table III. Our full three-view MV-FER framework achieves an accuracy of 89.54%, exceeding the recent strong published works: Guided Interpretable FER (88.95%) by +0.59 percentage points and EAC (88.75%) by +0.79 percentage points. Because these comparisons use the same ResNet-50 backbone as several competitors, the gains can be attributed to our multi-view learning design rather than architectural capacity.
Notably, the two-view variant (without any landmark information) already attains 88.75%, matching the best landmark-free baseline (EAC, 88.75\%). When landmarks are introduced only as structured supervision (not as input features), MV-FER rises to 89.54%, outperforming all landmark-based methods, including pACNN, RAN, OADN, FERGCN, and Guided Interpretable FER.
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IEEE/ICACT20260151 Slide.14
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