SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition
This work addresses the challenging problem of micro-expression recognition for applications in psychology and security, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled micro-expression recognition by proposing a dynamic segmented sparse imaging module and a segmented movement-attending spatiotemporal network to reveal subtle facial movement changes, achieving better performance than state-of-the-art methods on benchmarks like CASME II, SAMM, and SHIC.
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.