Dual-Inception Network for Cross-Database Micro-Expression Recognition

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.