Microexpression recognition algorithm based on multi feature fusion

BaiYang Xiang; BoKai Li; HuaiJuan Zang; ZeLiang Zhao; Shu Zhan

doi:10.1117/12.3014469

9 January 2024 Microexpression recognition algorithm based on multi feature fusion

BaiYang Xiang, BoKai Li, HuaiJuan Zang, ZeLiang Zhao, Shu Zhan

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296908 (2024) https://doi.org/10.1117/12.3014469
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

BaiYang Xiang, BoKai Li, HuaiJuan Zang, ZeLiang Zhao, and Shu Zhan "Microexpression recognition algorithm based on multi feature fusion", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296908 (9 January 2024); https://doi.org/10.1117/12.3014469

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KEYWORDS

Video

Feature extraction

Feature fusion

Detection and tracking algorithms

Education and training

Cross validation

Data modeling

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