Weiwei Xu, Zilin Li, Xuanqi Zhao, Yiran Zhu
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Facial emotion recognition (FER) models trained only on pixels often fail to generalize across datasets because facial appearance is an indirect-and biased-proxy for underlying affect. We present NeuroGaze-Distill, a cross-modal distillation framework that transfers brain-informed priors into an image-only FER student via static Valence-Arousal (V/A) prototypes and a depressioninspired geometric prior (D-Geo). A teacher trained on EEG topographic maps from DREAMER and MAHNOB-HCI produces a consolidated 5×5 V/A prototype grid that is frozen and reused; no EEG-face pairing and no non-visual signals at deployment are required. The student (ResNet-18/50) is trained on FERPlus with conventional CE/KD and two lightweight regularizers: (i) Proto-KD (cosine) aligns student features to the static prototypes; (ii) D-Geo softly shapes the embedding geometry in line with affective findings often reported in depression research (e.g., anhedonia-like contraction in high-valence regions). We evaluate both within-domain (FERPlus validation) and cross-dataset protocols (AffectNet-mini; optional CK+), reporting standard 8-way scores alongside present-only Macro-F1 and balanced accuracy to fairly handle label-set mismatch. Ablations attribute consistent gains to prototypes and D-Geo, and favor 5×5 over denser grids for stability. The method is simple, deployable, and improves robustness without architectural complexity. * First author. † This work was completed while the author was affiliated with the School of Computer Science and Technology. Note on the name. "NeuroGaze-Distill" emphasizes neuro-informed distillation. Gaze heatmaps are optional and may be disabled in the final experiments; the "Gaze" term survives to reflect the broader privileged-signal design.
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