Name
Abstract | ||
|---|---|---|
This paper presents the technique for our contribution to 2019 Micro-Expression Grand Challenge ( MEGC 2019). One sub-challenge of MEGC 2019 named Cross-Database ( Cross-DB) challenge aims to classify three main classes ( Negative, Positive and Surprise) in the task of Composite Database Evaluation ( CDE). Our proposed method utilizes Inception technique to overcome the challenge for the cross-database micro-expression recognition and can be divided into three steps. ( 1) In the preprocessing stage, onset and mid-position frames of each micro-expression sample are selected for the feature extraction. ( 2) TV-L1 optical flow features are calculated by the two frames obtained in the first step. ( 3) The horizontal and vertical components of TV-L1 optical flow features are fed to a Dual-Inception network for the micro-expression recognition. Our experiment results on three benchmark databases show that our proposed mechanism archives the overall unweighted F1 score ( UF1) of 0.7322 and unweighted average recall ( UAR) of 0.7278, which significantly outperform those metrics of the baseline method ( UF1: 0.5882, UAR: 0.5785). Code is publicly available on GitHub: https://github.com/xly135846/MEGC2019 |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/FG.2019.8756579 | 2019 14TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2019) |
Keywords | Field | DocType |
Micro-expression, cross-database, deep learning | F1 score,Horizontal and vertical,Facial expression recognition,Computer science,Feature extraction,Preprocessor,Artificial intelligence,Deep learning,Optical flow,Database | Conference |
ISSN | Citations | PageRank |
2326-5396 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Zhou | 1 | 0 | 1.01 |
| Qirong Mao | 2 | 261 | 34.29 |
| Luoyang Xue | 3 | 2 | 1.36 |