LP²DH jointly hashes spatiotemporal pixel-difference vectors with locality preservation and Stiefel-manifold optimization to produce compact binary features that achieve state-of-the-art accuracy on UCLA, DynTex++, and YUPENN dynamic texture benchmarks.
Sponta- neous facial micro-expression analysis using spatiotemporal completed local quantized patterns,
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MEDN decouples explicit motion and implicit emotion features with a dual-branch design, AU restriction, orthogonal loss, SEVit, and adaptive fusion to improve micro-expression recognition on benchmarks.
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LP$^{2}$DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition
LP²DH jointly hashes spatiotemporal pixel-difference vectors with locality preservation and Stiefel-manifold optimization to produce compact binary features that achieve state-of-the-art accuracy on UCLA, DynTex++, and YUPENN dynamic texture benchmarks.
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MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition
MEDN decouples explicit motion and implicit emotion features with a dual-branch design, AU restriction, orthogonal loss, SEVit, and adaptive fusion to improve micro-expression recognition on benchmarks.