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.
@article{arxiv.1904.03699,
title = {A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition},
author = {Min Peng and Chongyang Wang and Tao Bi and Tong Chen and XiangDong Zhou and Yu shi},
journal= {arXiv preprint arXiv:1904.03699},
year = {2019}
}