Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals

• URL: http://arxiv.org/abs/2308.09013v1
• Date: Thu, 17 Aug 2023 14:37:35 GMT
• Title: Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals
• Authors: Antoine Dubois, Carlos Lima Azevedo, Sonja Haustein and Bruno Miranda
• Abstract summary: This article proposes an unsupervised deep cluster framework for emotion recognition from physiological and psychological data. Tests on the open benchmark data set WESAD show that deep k-means and deep c-means distinguish the four quadrants of Russell's circumplex model of affect with an overall accuracy of 87%.
• Score: 1.5695847325697105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotions play a significant role in the cognitive processes of the human brain, such as decision making, learning and perception. The use of physiological signals has shown to lead to more objective, reliable and accurate emotion recognition combined with raising machine learning methods. Supervised learning methods have dominated the attention of the research community, but the challenge in collecting needed labels makes emotion recognition difficult in large-scale semi- or uncontrolled experiments. Unsupervised methods are increasingly being explored, however sub-optimal signal feature selection and label identification challenges unsupervised methods' accuracy and applicability. This article proposes an unsupervised deep cluster framework for emotion recognition from physiological and psychological data. Tests on the open benchmark data set WESAD show that deep k-means and deep c-means distinguish the four quadrants of Russell's circumplex model of affect with an overall accuracy of 87%. Seeding the clusters with the subject's subjective assessments helps to circumvent the need for labels.

Related papers

• Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review [1.8310098790941458]
  The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. The development of AI systems for discerning complex emotions poses a substantial challenge with significant implications for affective computing. incorporating physiological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) can notably enhance CERS.
  arXiv  Detail & Related papers  (2024-09-09T05:06:10Z)
• Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition [23.505616142198487]
  We develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition. The model learns universal latent representations of EEG signals through pre-training on large scale dataset. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks.
  arXiv  Detail & Related papers  (2024-05-28T14:31:11Z)
• Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning [3.2536246345549538]
  We propose ME-MHACL, a self-supervised contrastive learning-based multimodal emotion recognition method. We apply the trained feature extractor to labeled electrophysiological signals and use multi-head attention mechanisms for feature fusion. Our method outperformed existing benchmark methods in emotion recognition tasks and had good cross-individual generalization ability.
  arXiv  Detail & Related papers  (2023-07-12T05:55:40Z)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly [15.217754542927961]
  Early detection of cognitive impairment is of great importance to both patients and caregivers. It has been found that patients with cognitive impairment show abnormal emotion patterns. We present a novel deep convolution network-based system to detect the cognitive impairment.
  arXiv  Detail & Related papers  (2021-11-09T11:51:33Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

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.