SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition

• URL: http://arxiv.org/abs/2010.09342v1
• Date: Mon, 19 Oct 2020 09:23:24 GMT
• Title: SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition
• Authors: Jiateng Liu, Wenming Zheng, Yuan Zong
• Abstract summary: This paper proposes a segmented movement-attending network (SMA-STN) to reveal subtle movement changes visually in an efficient way. Extensive experiments on three widely used benchmarks, i.e., CALoss II, SAMM, and SHIC, show that the proposed SMA-STN achieves better MER performance than other state-of-the-art methods.
• Score: 20.166205708651194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER). To handle the crucial issue, in this paper, we firstly propose a dynamic segmented sparse imaging module (DSSI) to compute dynamic images as local-global spatiotemporal descriptors under a unique sampling protocol, which reveals the subtle movement changes visually in an efficient way. Secondly, a segmented movement-attending spatiotemporal network (SMA-STN) is proposed to further unveil imperceptible small movement changes, which utilizes a spatiotemporal movement-attending module (STMA) to capture long-distance spatial relation for facial expression and weigh temporal segments. Besides, a deviation enhancement loss (DE-Loss) is embedded in the SMA-STN to enhance the robustness of SMA-STN to subtle movement changes in feature level. Extensive experiments on three widely used benchmarks, i.e., CASME II, SAMM, and SHIC, show that the proposed SMA-STN achieves better MER performance than other state-of-the-art methods, which proves that the proposed method is effective to handle the challenging MER problem.

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