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