Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition


Yang, John See Su and Khor, Huai Qian and Liong, Sze Teng and Gan, Y S and Huang, Yen Chang (2019) Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition. In: 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, 14-18 May 2019, Lille, France.

[img] Text
268.pdf - Published Version
Restricted to Repository staff only

Download (302kB)


In the recent year, state-of-the-art for facial micro-expression 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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Face recognition
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 20 Sep 2021 03:19
Last Modified: 20 Sep 2021 03:19


Downloads per month over past year

View ItemEdit (login required)