Name
Abstract | ||
|---|---|---|
The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using the apex frame is able to get rid of redundant video frames, but the relevant temporal evidence of micro-expression would be thereby left out. This paper proposes a novel Apex-Time Network (ATNet)to recognize micro-expression based on spatial information from the apex frame as well as on temporal information from the respective-adjacent frames. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by learning such temporal information. Specially, the model with such temporal information is more robust in cross-dataset validations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACII.2019.8925525 | 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) |
Keywords | Field | DocType |
micro-expression,deep learning,neural network,feature fusion | Spatial analysis,Computer vision,Facial recognition system,Communication,Facial expression recognition,Computer science,Apex (geometry),Feature extraction,Artificial intelligence,Deep learning,Artificial neural network,Optical computing | Conference |
ISSN | ISBN | Citations |
2156-8103 | 978-1-7281-3889-3 | 3 |
PageRank | References | Authors |
0.38 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Peng | 1 | 115 | 19.12 |
| Chongyang Wang | 2 | 6 | 1.42 |
| tao bi | 3 | 7 | 2.45 |
| Yu Shi | 4 | 3 | 1.39 |
| Xiang-Dong Zhou | 5 | 180 | 16.85 |
| Tong Chen | 6 | 10 | 3.92 |