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Publication Detail
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
  • Publication Type:
    Conference
  • Authors:
    Peng M, Wang C, Bi T, Chen T, Zhou X, shi Y
  • Publisher:
    IEEE
  • Publication date:
    03/09/2019
  • Published proceedings:
    Proceedings of 8th International Conference on Affective Computing and Intelligent Interaction
  • Name of conference:
    8th International Conference on Affective Computing and Intelligent Interaction (ACII)
  • Conference place:
    Cambridge, UK
  • Conference start date:
    03/09/2019
  • Conference finish date:
    05/09/2019
  • Keywords:
    cs.CV
  • Notes:
    6 pages, 3 figures, 3 tables, codes available
Abstract
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.
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