Decomposed Spatial Modulator for Efficient Facial-Expression Recognition

초록

The balance between accuracy and latency is important in facial-expression recognition, particularly in real-time applications where facial expressions can change quickly. Feature modulators are an effective approach for boosting efficiency as they amplify expression-relevant information while keeping computational costs low. However, existing modulators face two prevalent challenges: (i) pooling-based approaches flatten the spatial structure and lose positional information essential for facial expressions and (ii) pooling-free approaches frequently require substantial computational resources owing to long convolution chains, multibranch architectures, or high-dimensional gating mechanisms. In this letter, we propose an efficient modulator that emphasizes spatial refinement with minimal overhead. Specifically, the modulator avoids spatial-resolution collapse by generating a spatial gate directly on the full-resolution spatial grid without global pooling. The modulator adopts a single lightweight branch comprising two convolutions: a large-kernel convolution for context aggregation and a standard convolution that outputs a single-channel score map for spatial gating. Experiments on RAF-DB, FER2013, CK+, and AffectNet demonstrate that the proposed model achieves an effective accuracy–latency balance compared with recent models.

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