University of Oulu

Thuong-Khanh Tran, Quang-Nhat Vo, and Guoying Zhao. 2021. DynGeoNet: Fusion Network for Micro-expression Spotting. Proceedings of the 2021 International Conference on Multimodal Interaction. Association for Computing Machinery, New York, NY, USA, 745–749. DOI:

DynGeoNet : fusion network for micro-expression spotting

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Author: Tran, Thuong-Khanh1; Vo, Quang-Nhat2; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu Oulu, Finland
2Silo.AI, Helsinki, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Association for Computing Machinery, 2021
Publish Date: 2022-01-26


Micro-expressions (MEs) are brief and involuntary facial expressions when people hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. However, the ME analysis system can still not work well in the real environment because of the challenging performance of ME spotting, which is to spot the images with micro-expressions from long video sequences. To improve the performance of ME spotting, we focus on hybrid feature engineering, which aims to create a robust feature for discriminating tiny movements. The proposed framework consists of two main modules: (1) the feature engineering extracts both geometric features and appearance features based on dynamic image; (2) the new deep neural network inputs the handcrafted feature for the late fusion and ME samples classification. Our experimental results from three baseline datasets demonstrate the promising results.

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ISBN: 978-1-4503-8481-0
Pages: 745 - 749
DOI: 10.1145/3462244.3479958
Host publication: 23rd ACM International Conference on Multimodal Interaction, ICMI 2021
Conference: ACM International Conference on Multimodal Interaction
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work was supported by Infotech Oulu, Ministry of Education and Culture of Finland for AI forum project, and Academy of Finland for ICT 2023 project (grant 328115). As well, the authors wish to acknowledge CSC IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 328115
Detailed Information: 328115 (Academy of Finland Funding decision)
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