Shallow Optical Flow Three-Stream CNN For Macro- And Micro-Expression Spotting From Long Videos

Gen Bing Liong, John See, Lai Kuan Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Facial expressions vary from the visible to the subtle. In recent years, the analysis of micro-expressions— a natural occurrence resulting from the suppression of one’s true emotions, has drawn the attention of researchers with a broad range of potential applications. However, spotting micro-expressions in long videos becomes increasingly challenging when intertwined with normal or macro-expressions. In this paper, we propose a shallow optical flow three-stream CNN (SOFTNet) model to predict a score that captures the likelihood of a frame being in an expression interval. By fashioning the spotting task as a regression problem, we introduce pseudo-labeling to facilitate the learning process. We demonstrate the efficacy and efficiency of the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art performance is achieved on CAS(ME)2 with equally promising results on SAMM Long Videos.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing
PublisherIEEE
Pages2643-2647
Number of pages5
ISBN (Electronic)9781665441155
DOIs
Publication statusPublished - 23 Aug 2021
Event28th IEEE International Conference on Image Processing 2021 - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021
https://www.2021.ieeeicip.org/

Conference

Conference28th IEEE International Conference on Image Processing 2021
Abbreviated title2021 IEEE ICIP
Country/TerritoryUnited States
CityAnchorage
Period19/09/2122/09/21
Internet address

Keywords

  • Macro-expression
  • Micro-expression
  • Optical flow
  • Shallow CNN
  • Spotting

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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