Name
Abstract | ||
|---|---|---|
Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2020.06.005 | Neurocomputing |
Keywords | DocType | Volume |
Micro expression recognition,Deep learning,Attention mechanism,Transfer learning | Journal | 410 |
ISSN | Citations | PageRank |
0925-2312 | 3 | 0.37 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chongyang Wang | 1 | 6 | 1.42 |
| Min Peng | 2 | 115 | 19.12 |
| tao bi | 3 | 7 | 2.45 |
| Tong Chen | 4 | 10 | 3.92 |