Micro-expression recognition from local facial regions

Aouayeb, Mouath; Hamidouche, Wassim; Soladié, Catherine; Kpalma, Kidiyo; Séguier, Renaud

doi:10.1016/j.image.2021.116457

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…It can realize learning at the temporal sequence level without consuming too much performance. Compared with some traditional temporal sequence algorithms, such as Long Short Term Memory (LSTM) [20] , Temporal Correlation Network (TRN) [21] , its performance has improved to a certain extent Before introducing this method, we first define a video frame as shown in Figure 1.…”

Section: Temporal Modulementioning

confidence: 99%

Microexpression recognition algorithm based on multi feature fusion

XIANG,

LI,

ZANG

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Video facial micro expression recognition is difficult to extract features due to its short duration and small action amplitude. In order to better combine temporal and spatial information of video, the whole model is divided into local attention module, global attention module and temporal module. First, the local attention module intercepts the key areas and sends them to the network with channel attention after processing; Then the global attention module sends the data into the network with spatial attention after random erasure avoiding key areas; Finally, the temporal module sends the micro expression occurrence frame to the network with temporal shift module and spatial attention after processing; Finally, the classification results are obtained through three full connection layers after feature fusion. The experiment is tested based on CASMEⅡ dataset,After five-fold Cross Validation, the average accuracy rate is 76.15, the unweighted F1 value is 0.691.Compared with the mainstream algorithm, this method has improvement.

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Section: Temporal Modulementioning

confidence: 99%