학술논문

Geometric Distance for Fast Micro-Expression Detection
Document Type
Conference
Source
2020 IEEE 20th International Conference on Communication Technology (ICCT) Communication Technology (ICCT), 2020 IEEE 20th International Conference on. :1405-1411 Oct, 2020
Subject
Communication, Networking and Broadcast Technologies
Feature extraction
Mouth
Faces
Euclidean distance
Nose
Lips
Face recognition
Geometric distance
micro-expression detection
support vector machine
Language
ISSN
2576-7828
Abstract
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.