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The automatic recognition of micro-expression has been boosted ever since thesuccessful introduction of deep learning approaches. As researchers working onsuch topics are moving to learn from the nature of micro-expression, thepractice of using deep learning techniques has evolved from processing theentire video clip of micro-expression to the recognition on apex frame. Usingthe apex frame is able to get rid of redundant video frames, but the relevanttemporal evidence of micro-expression would be thereby left out. This paperproposes a novel Apex-Time Network (ATNet) to recognize micro-expression basedon spatial information from the apex frame as well as on temporal informationfrom the respective-adjacent frames. Through extensive experiments on threebenchmarks, we demonstrate the improvement achieved by learning such temporalinformation. Specially, the model with such temporal information is more robustin cross-dataset validations.

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