Facial Micro-Expressions Grand Challenge 2018: Evaluating Spatio-Temporal Features for Classification of Objective Classes

摘要

This paper presents baseline results for the first Facial Micro-expressions Grand Challenge (MEGC) 2018 by evaluating LBP-TOP, HOOF and 3DHOG on CASME II and SAMM. We further improve the result of composite database evaluation (Task B of the challenge) by introducing selective block-based features fusion representation. Base on objective classes, this task combines CASME II and SAMM into a single composite database and uses Leave-One-Subject-Out crossvalidation to evaluate the performance. Our proposed method achieve F1-Score of 0.579, which outperformed LBP-TOP, HOOF and 3DHOG with 0.523, 0.527 and 0.436, respectively.