Open AccessTSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting
Author(s) -
Zhihao Zhang,
Fan Mo,
Ke Zhao,
Tong Chen,
Xiaolan Fu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012028
Subject(s) - spotting , feature (linguistics) , expression (computer science) , significant difference , domain (mathematical analysis) , facial expression recognition , pattern recognition (psychology) , psychology , artificial intelligence , computer science , mathematics , statistics , linguistics , facial recognition system , programming language , mathematical analysis , philosophy
The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.