Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition

• URL: http://arxiv.org/abs/2406.08997v1
• Date: Thu, 13 Jun 2024 10:57:24 GMT
• Title: Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition
• Authors: Fengyuan Zhang, Zhaopei Huang, Xinjie Zhang, Qin Jin,
• Abstract summary: 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.
• Score: 48.21696443824074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Micro-expressions serve as essential cues for understanding individuals' genuine emotional states. Recognizing micro-expressions attracts increasing research attention due to its various applications in fields such as business negotiation and psychotherapy. However, the intricate and transient nature of micro-expressions poses a significant challenge to their accurate recognition. Most existing works either neglect temporal dependencies or suffer from redundancy issues in clip-level recognition. In this work, 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. Specifically, the integration of Adaptive Temporal Motion layers empowers our method to aggregate global and local motion features inherent in micro-expressions. Experimental results demonstrate that ATM-GCN not only surpasses existing state-of-the-art methods, particularly on the Composite dataset, but also achieves superior performance on the latest micro-expression dataset CAS(ME)$^3$.

Related papers

• Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition [21.675660978188617]
  Micro-expression recognition is crucial in many fields, including criminal analysis and psychotherapy. A three-stream temporal-shift attention network based on self-knowledge distillation called SKD-TSTSAN is proposed in this paper.
  arXiv  Detail & Related papers  (2024-06-25T13:22:22Z)
• Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning [22.525295392858293]
  We propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively.
  arXiv  Detail & Related papers  (2022-05-29T12:28:10Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)
• Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms [52.58031087639394]
  micro-expressions are involuntary and transient facial expressions. They can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, their detection and recognition is difficult and relies heavily on expert experiences.
  arXiv  Detail & Related papers  (2022-01-30T05:14:13Z)
• MMNet: Muscle motion-guided network for micro-expression recognition [2.032432845751978]
  We propose a robust micro-expression recognition framework, namely muscle motion-guided network (MMNet) Specifically, a continuous attention (CA) block is introduced to focus on modeling local subtle muscle motion patterns with little identity information. Our approach outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-01-14T04:05:49Z)
• 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)
• Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos [55.52369116870822]
  This paper focuses on tackling the problem of temporal language localization in videos. It aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video.
  arXiv  Detail & Related papers  (2021-10-12T14:59:25Z)
• Temporal Memory Relation Network for Workflow Recognition from Surgical Video [53.20825496640025]
  We propose a novel end-to-end temporal memory relation network (TMNet) for relating long-range and multi-scale temporal patterns. We have extensively validated our approach on two benchmark surgical video datasets.
  arXiv  Detail & Related papers  (2021-03-30T13:20:26Z)
• Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining [18.34213657996624]
  In micro-expression, facial movement is transient and sparsely localized through time. We propose a novel end-to-end deep learning architecture, referred to as adaptive key-frame mining network (AKMNet) AKMNet is able to learn discriminative-temporal representation by combining spatial features of self-learned local key frames and their global-temporal dynamics.
  arXiv  Detail & Related papers  (2020-09-19T07:03:16Z)

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.