Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition
Wei, Jinsheng; Peng, Wei; Lu, Guanming; Li, Yante; Yan, Jingjie; Zhao, Guoying (2023-12-06)
IEEE
06.12.2023
J. Wei, W. Peng, G. Lu, Y. Li, J. Yan and G. Zhao, "Geometric Graph Representation With Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition," in IEEE Transactions on Affective Computing, vol. 15, no. 3, pp. 1343-1357, July-Sept. 2024, doi: 10.1109/TAFFC.2023.3340016
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403212377
https://urn.fi/URN:NBN:fi:oulu-202403212377
Tiivistelmä
Abstract
Micro-expression recognition (MER) holds significance in uncovering hidden emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a lowdimensional and compact modality, which achieves lower computational cost and potentially concentrates on ME-related movement features. However, the discriminability of facial landmarks for MER is unclear. Thus, this paper investigates the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs with facial landmarks. Firstly, a geometric twostream graph network is constructed to aggregate the low-order and high-order geometric movement information from facial landmarks to obtain discriminative ME representation. Secondly, a self-learning fashion is introduced to automatically model the dynamic relationship between nodes even long-distance nodes. Furthermore, an adaptive action unit loss is proposed to reasonably build a strong correlation between landmarks, facial action units and MEs. Notably, this work provides a novel idea with much higher efficiency to promote MER, only utilizing graphbased geometric features. The experimental results demonstrate that the proposed method achieves competitive performance with a significantly reduced computational cost. Furthermore, facial landmarks significantly contribute to MER and are worth further study for high-efficient ME analysis.
Micro-expression recognition (MER) holds significance in uncovering hidden emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a lowdimensional and compact modality, which achieves lower computational cost and potentially concentrates on ME-related movement features. However, the discriminability of facial landmarks for MER is unclear. Thus, this paper investigates the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs with facial landmarks. Firstly, a geometric twostream graph network is constructed to aggregate the low-order and high-order geometric movement information from facial landmarks to obtain discriminative ME representation. Secondly, a self-learning fashion is introduced to automatically model the dynamic relationship between nodes even long-distance nodes. Furthermore, an adaptive action unit loss is proposed to reasonably build a strong correlation between landmarks, facial action units and MEs. Notably, this work provides a novel idea with much higher efficiency to promote MER, only utilizing graphbased geometric features. The experimental results demonstrate that the proposed method achieves competitive performance with a significantly reduced computational cost. Furthermore, facial landmarks significantly contribute to MER and are worth further study for high-efficient ME analysis.
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