Micro-expression recognition based on multiscale convolutional fusion and capsule network

Jiawei Fan; Zhihua Xie; Shijia Cheng

doi:10.1117/12.2644615

10 November 2022 Micro-expression recognition based on multiscale convolutional fusion and capsule network

Jiawei Fan, Zhihua Xie, Shijia Cheng

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123012A (2022) https://doi.org/10.1117/12.2644615
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

Due to the short duration and low intensity, the efficient feature learning is a big challenge for robust facial microexpression (ME) recognition. To achieve the diverse and spatial relation representation, this paper proposes a simple yet effective micro-expression recognition method based on multiscale convolutional fusion and capsule network (MCFCN). Firstly, the apex frame in a ME clip is located by computing the pixel difference of frames, and then the apex frame is processed by an optical flow operator. Secondly, a multi-scale fusion module is introduced to capture diverse ME related details. Then, the micro-expression features are fed into the capsule network for a good description about spatial relation. Finally, the entire ME recognition model is trained and verified on three popular benchmarks (SAMM, SMIC and CASMEII) using the associated standard evaluation protocols. Experimental results show that our method based on MCFCN is superior to the works based on pervious capsule network or other state-of-the-art CNN models.

Citation Download Citation

Jiawei Fan, Zhihua Xie, and Shijia Cheng "Micro-expression recognition based on multiscale convolutional fusion and capsule network", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123012A (10 November 2022); https://doi.org/10.1117/12.2644615

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KEYWORDS

Convolution

Optical flow

Feature extraction

Performance modeling

Information fusion

Image fusion

Data modeling

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