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

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...

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Bibliographic Details
Published inInternational Conference on Affective Computing and Intelligent Interaction and workshops pp. 1 - 6
Main Authors Peng, Min, Wang, Chongyang, Bi, Tao, Shi, Yu, Zhou, Xiangdong, Chen, Tong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
deep learning
Face recognition
Feature extraction
feature fusion
Machine learning
micro-expression
neural network
Neural networks
Optical computing
Optical imaging
Streaming media
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Summary: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.
ISSN:2156-8111
DOI:10.1109/ACII.2019.8925525