A Novel Combined Optical Flow Approach for Comprehensive Micro-Expression Recognition

• URL: http://arxiv.org/abs/2510.15471v1
• Date: Fri, 17 Oct 2025 09:29:17 GMT
• Title: A Novel Combined Optical Flow Approach for Comprehensive Micro-Expression Recognition
• Authors: Vu Tram Anh Khuong, Thi Bich Phuong Man, Luu Tu Nguyen, Thanh Ha Le, Thi Duyen Ngo,
• Abstract summary: This study introduces a Combined Optical Flow (COF), integrating both phases to enhance feature representation.<n> Experimental results on CASMEII and SAMM datasets show that COF outperforms single optical flow-based methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial micro-expressions are brief, involuntary facial movements that reveal hidden emotions. Most Micro-Expression Recognition (MER) methods that rely on optical flow typically focus on the onset-to-apex phase, neglecting the apex-to-offset phase, which holds key temporal dynamics. This study introduces a Combined Optical Flow (COF), integrating both phases to enhance feature representation. COF provides a more comprehensive motion analysis, improving MER performance. Experimental results on CASMEII and SAMM datasets show that COF outperforms single optical flow-based methods, demonstrating its effectiveness in capturing micro-expression dynamics.

Related papers

• Improving Micro-Expression Recognition with Phase-Aware Temporal Augmentation [0.0]
  Micro-expressions (MEs) are brief, involuntary facial movements that reveal genuine emotions, typically lasting less than half a second.<n>Deep learning has enabled significant advances in micro-expression recognition (MER), but its effectiveness is limited by the scarcity of annotated ME datasets.<n>This paper proposes a phase-aware temporal augmentation method based on dynamic image.
  arXiv  Detail & Related papers  (2025-10-17T09:20:51Z)
• DIANet: A Phase-Aware Dual-Stream Network for Micro-Expression Recognition via Dynamic Images [0.0]
  Micro-expressions are brief, involuntary facial movements that typically last less than half a second and often reveal genuine emotions.<n>This paper proposes a novel dual-stream framework, DIANet, which leverages phase-aware dynamic images.<n>Experiments conducted on three benchmark MER datasets demonstrate that the proposed method consistently outperforms conventional single-phase DI-based approaches.
  arXiv  Detail & Related papers  (2025-10-14T07:15:29Z)
• FMANet: A Novel Dual-Phase Optical Flow Approach with Fusion Motion Attention Network for Robust Micro-expression Recognition [0.0]
  micro-expression recognition is challenging due to the difficulty of capturing subtle facial movements.<n>We introduce a comprehensive motion representation, which integrates motion dynamics from both micro-expression phases into a unified descriptor.<n>We then propose FMANet, a novel end-to-end neural network architecture that internalizes the dual-phase analysis and magnitude modulation into learnable modules.
  arXiv  Detail & Related papers  (2025-10-09T05:36:40Z)
• MPT: Motion Prompt Tuning for Micro-Expression Recognition [47.62949098749473]
  This paper introduces Motion Prompt Tuning (MPT) as a novel approach to adapting pre-training models for micro-expression recognition (MER)<n>MPT represents a pioneering method for subtle motion prompt tuning. Particularly, we introduce motion prompt generation, including motion magnification and Gaussian tokenization, to extract subtle motions as prompts for LMs.<n>Extensive experiments conducted on three widely used MER datasets demonstrate that our proposed MPT consistently surpasses state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-08-13T02:57:43Z)
• MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution [46.600316142855334]
  Facial micro-expression recognition (MER) is a challenging problem, due to transient and subtle micro-expression (ME) actions.<n>We propose an end-to-end micro-action-aware deep learning framework with advantages from transformer, graph convolution, and vanilla convolution.<n>Our framework outperforms the state-of-the-art MER methods on CASME II, SAMM, and SMIC benchmarks.
  arXiv  Detail & Related papers  (2025-06-17T13:35:06Z)
• MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception [53.00485107136624]
  Micro-expressions (MEs) are brief and low-intensity facial movements revealing concealed emotions.<n>We propose a ME Large Language Model (MELLM) that integrates optical flow-based sensitivity to subtle facial motions.<n>MELLM achieves state-of-the-art accuracy and generalization across multiple ME benchmarks.
  arXiv  Detail & Related papers  (2025-05-11T15:08:23Z)
• AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition [15.008358563986825]
  We design an Adaptive Hierarchical Multi-Scale Attention Network (AHMSA-Net) for micro-expression recognition.<n> AHMSA-Net consists of two parts: an adaptive hierarchical framework and a multi-scale attention mechanism.<n>Experiments demonstrate that AHMSA-Net achieves recognition accuracy of up to 78.21% on composite databases.
  arXiv  Detail & Related papers  (2025-01-05T13:40:12Z)
• Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition [48.21696443824074]
  We propose a novel framework for micro-expression recognition, named the Adaptive Temporal Motion Guided Graph Convolution Network (ATM-GCN) Our framework excels at capturing temporal dependencies between frames across the entire clip, thereby enhancing micro-expression recognition at the clip level.
  arXiv  Detail & Related papers  (2024-06-13T10:57:24Z)
• Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition [61.374467942519374]
  We propose a novel a-temporal transformer architecture -- to the best of our knowledge, the first purely transformer based approach for micro-expression recognition. The architecture comprises a spatial encoder which learns spatial patterns, a temporal dimension classification for temporal analysis, and a head. A comprehensive evaluation on three widely used spontaneous micro-expression data sets, shows that the proposed approach consistently outperforms the state of the art.
  arXiv  Detail & Related papers  (2021-12-10T22:10:31Z)
• Direct phase modulation via optical injection: theoretical study [50.591267188664666]
  We study the influence of the spontaneous emission noise, examine the role of the gain non-linearity and consider the effect of the temperature drift. We have tried to formulate here practical instructions, which will help to take these features into account when elaborating and employing the optical-injection-based phase modulator.
  arXiv  Detail & Related papers  (2020-11-18T13:20:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.