A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
This work addresses the problem of recognizing subtle facial expressions for applications in psychology and security, representing an incremental improvement over existing deep learning methods.
The paper tackles micro-expression recognition by proposing the Apex-Time Network (ATNet), which combines spatial information from apex frames with temporal information from adjacent frames, achieving improved robustness in cross-dataset validations.
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.