# A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition

arXiv: https://arxiv.org/abs/1904.03699 · HTML: https://mlsanity.com/paper/1904.03699/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition](https://mlsanity.com/paper/2101.04838/) (2021)
- [Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition](https://mlsanity.com/paper/2112.05851/) (2021)
- [Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals](https://mlsanity.com/paper/2308.12156/) (2023)
- [Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being](https://mlsanity.com/paper/2502.17172/) (2025)
- [FaceSleuth-R: Adaptive Orientation-Aware Attention for Robust Micro-Expression Recognition](https://mlsanity.com/paper/2506.02695/) (2025)

## Abstract (arXiv)

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.