Geometric Distance for Fast Micro-Expression Detection

H. Lu (1) , D. Li (1) , Kidiyo Kpalma (2) , M. Yang (3)

1 Shandong Normal University
2 IETR - Institut d'Électronique et des Technologies du numéRique
3 Shandong University

In this paper, we propose a new model for micro-expression detection in videos in conjunction with a set of facial keypoints. The main contribution lies at the construction of geometric features extracted using the geometric distances between pairs of keypoints in different groups. The proposed geometric features-based micro-expression detection model requires a very low computation complexity and simultaneously attains highly accurate detection rates. We compare the proposed geometric features-based model to the existing state-of-the-art micro-expression detection and recognition models in three datasets, where the proposed method has achieved better, or at least comparably accurate results but requiring much less computation time. © 2020 IEEE.

Mots clés: Geometric distance; micro-expression detection; support vector machine

Domaines: Sciences de l'ingénieur [physics]

H. Lu, D. Li, Kidiyo Kpalma, M. Yang. Geometric Distance for Fast Micro-Expression Detection. 20th IEEE International Conference on Communication Technology, ICCT 2020, Oct 2020, Nanning, China. pp.1405-1411, ⟨10.1109/ICCT50939.2020.9295694⟩. ⟨hal-03130050⟩