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

M Peng, Chongyang Wang, Tao Bi, T Chen, X Zhou, Y shi
in 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Conference paper (text), Cambridge, UK


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