A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework
원문 초록
Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facialexpressions. Automatic MER is challenging because that 1) the intensity of subtle facial muscle movement is extremely lowand 2) the duration of ME is transient.Recent works adopt motion magnification or time interpolation to resolve these issues. Nevertheless, existing works dividethem into two separate modules due to their non-linearity. Though such operation eases the difficulty in implementation, itignores their underlying connections and thus results in inevitable losses in both accuracy and speed. Instead, in this paper, weexplore their underlying joint formulations and propose a consolidated Eulerian framework to reveal the subtle facial movements.It expands the temporal duration and amplifies the muscle movements in micro-expressions simultaneously. Compared toexisting approaches, the proposed method can not only process ME clips more efficiently but also make subtle ME movementsmore distinguishable. Experiments on two public MER databases indicate that our model outperforms the state-of-the-art inboth speed and accuracy.
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Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition
AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
A Benchmark for Incremental Micro-expression Recognition
토론
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