Shallow triple stream three-dimensional CNN (STSTNet) for micro-expression recognition

Sze-Teng Liong, Y. S. Gan, John See, Huai-Qian Khor, Yen-Chang Huang

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

33 Citations (Scopus)

Abstract

In the recent year, state-of-the-art for facial microexpression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.

Original languageEnglish
Title of host publication14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
PublisherIEEE
ISBN (Electronic)9781728100890
DOIs
Publication statusPublished - 11 Jul 2019
Event14th IEEE International Conference on Automatic Face and Gesture Recognition 2019 - Lille, France
Duration: 14 May 201918 May 2019

Conference

Conference14th IEEE International Conference on Automatic Face and Gesture Recognition 2019
Abbreviated titleFG 2019
Country/TerritoryFrance
CityLille
Period14/05/1918/05/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Media Technology

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