Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding

• URL: http://arxiv.org/abs/2405.13206v1
• Date: Tue, 21 May 2024 21:16:55 GMT
• Title: Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding
• Authors: Rong Gao, Xin Liu, Bohao Xing, Zitong Yu, Bjorn W. Schuller, Heikki Kälviäinen,
• Abstract summary: We focus on a special group of human body language -- the micro-gesture (MG) MG differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. We explore various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods.
• Score: 21.94739567923136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we focus on a special group of human body language -- the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explored various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient plug-and-play spatiotemporal balancing fusion method. We not only studied our method on the considered micro-gesture dataset but also conducted experiments on mainstream action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on micro-gestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. The scenarios we developed can be extended to other micro-gesture-based tasks such as deception detection and interviews. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence.

Related papers

• Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition [64.56321246196859]
  We propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework. We first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information. We introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes.
  arXiv  Detail & Related papers  (2024-11-18T05:16:11Z)
• MMAD: Multi-label Micro-Action Detection in Videos [23.508563348306534]
  We propose a new task named Multi-label Micro-Action Detection (MMAD) MMAD involves identifying all micro-actions in a given short video, determining their start and end times, and categorizing them. To support the MMAD task, we introduce a new dataset named Multi-label Micro-Action-52 (MMA-52), specifically designed to facilitate the detailed analysis and exploration of complex human micro-actions.
  arXiv  Detail & Related papers  (2024-07-07T09:45:14Z)
• 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)
• GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing [74.68232970965595]
  Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-09T13:56:25Z)
• Benchmarking Micro-action Recognition: Dataset, Methods, and Applications [26.090557725760934]
  Micro-action is imperceptible non-verbal behaviour characterised by low-intensity movement. In this study, we innovatively collect a new micro-action dataset designated as Micro-action-52 (MA-52) Uniquely, MA-52 provides the whole-body perspective including gestures, upper- and lower-limb movements.
  arXiv  Detail & Related papers  (2024-03-08T11:48:44Z)
• 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)
• 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)
• iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis [23.261770969903065]
  iMiGUE is identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE) iMiGUE focuses on micro-gesture, i.e., unintentional behaviors driven by inner feelings.
  arXiv  Detail & Related papers  (2021-07-01T08:15:14Z)
• Micro-expression spotting: A new benchmark [74.69928316848866]
  Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions. In the computer vision field, the study of MEs can be divided into two main tasks, spotting and recognition. This paper introduces an extension of the SMIC-E database, namely the SMIC-E-Long database, which is a new challenging benchmark for ME spotting.
  arXiv  Detail & Related papers  (2020-07-24T09:18:41Z)

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.