Name
Title | ||
|---|---|---|
FAMGAN: Fine-grained AUs Modulation based Generative Adversarial Network for Micro-Expression Generation |
Abstract | ||
|---|---|---|
ABSTRACTMicro-expressions (MEs) are significant and effective clues to reveal the true feelings and emotions of human beings, and thus MEs analysis is widely used in different fields such as medical diagnosis, interrogation and security. However, it is extremely difficult to elicit and label MEs, resulting in a lack of sufficient MEs data for MEs analysis. To address this challenge and inspired by the current face generation technology, in this paper we introduce Generative Adversarial Network based on fine-grained Action Units (AUs) modulation to generate MEs sequence (FAMGAN). Specifically, after comprehensively analyzing the factors that lead to inaccurate AU values detection, we performed fine-grained AUs modulation, which includes carefully eliminating the various noises and dealing with the asymmetry of AUs intensity. Additionally, we incorporate super-resolution into our model to enhance the quality of the generated images. Through experiments, we show that the system achieves very competitive results on the Micro-Expression Grand Challenge (MEGC2021). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3474085.3479212 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Xu | 1 | 4 | 2.07 |
| Sirui Zhao | 2 | 4 | 2.75 |
| Huaying Tang | 3 | 0 | 0.68 |
| Xinglong Mao | 4 | 0 | 0.68 |
| Tong Xu | 5 | 218 | 36.15 |
| Enhong Chen | 6 | 0 | 0.34 |