Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics

• URL: http://arxiv.org/abs/2503.16532v1
• Date: Tue, 18 Mar 2025 13:15:32 GMT
• Title: Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics
• Authors: Meisam Jamshidi Seikavandi, Jostein Fimland, Maria Barrett, Paolo Burelli,
• Abstract summary: In this study, we showcase how integrating eye-tracking data, temporal dynamics, and personality traits can substantially enhance the detection of both perceived and felt emotions.<n>Our findings inform the design of future affective computing and human-agent systems.
• Score: 1.4645774851707578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate emotion recognition is pivotal for nuanced and engaging human-computer interactions, yet remains difficult to achieve, especially in dynamic, conversation-like settings. In this study, we showcase how integrating eye-tracking data, temporal dynamics, and personality traits can substantially enhance the detection of both perceived and felt emotions. Seventy-three participants viewed short, speech-containing videos from the CREMA-D dataset, while being recorded for eye-tracking signals (pupil size, fixation patterns), Big Five personality assessments, and self-reported emotional states. Our neural network models combined these diverse inputs including stimulus emotion labels for contextual cues and yielded marked performance gains compared to the state-of-the-art. Specifically, perceived valence predictions reached a macro F1-score of 0.76, and models incorporating personality traits and stimulus information demonstrated significant improvements in felt emotion accuracy. These results highlight the benefit of unifying physiological, individual and contextual factors to address the subjectivity and complexity of emotional expression. Beyond validating the role of user-specific data in capturing subtle internal states, our findings inform the design of future affective computing and human-agent systems, paving the way for more adaptive and cross-individual emotional intelligence in real-world interactions.

Related papers

• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics. We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention. Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks. Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors [63.194053817609024]
  We introduce eye behaviors as an important emotional cues for the creation of a new Eye-behavior-aided Multimodal Emotion Recognition dataset. For the first time, we provide annotations for both Emotion Recognition (ER) and Facial Expression Recognition (FER) in the EMER dataset. We specifically design a new EMERT architecture to concurrently enhance performance in both ER and FER.
  arXiv  Detail & Related papers  (2024-11-08T04:53:55Z)
• Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation [5.534160116442057]
  The subjectivity of emotions poses significant challenges in developing accurate and robust computational models. This thesis examines critical facets of emotion recognition, beginning with the collection of diverse datasets. To handle the challenge of non-representative training data, this work collects the Multimodal Stressed Emotion dataset.
  arXiv  Detail & Related papers  (2023-09-06T02:45:42Z)
• Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers [0.0]
  We propose a framework that employs Swin Vision Transformers (SwinT) and squeeze and excitation block (SE) to address vision tasks. Our focus was to create an efficient FER model based on SwinT architecture that can recognize facial emotions using minimal data. We trained our model on a hybrid dataset and evaluated its performance on the AffectNet dataset, achieving an F1-score of 0.5420.
  arXiv  Detail & Related papers  (2023-01-26T02:29:17Z)
• A cross-corpus study on speech emotion recognition [29.582678406878568]
  This study investigates whether information learnt from acted emotions is useful for detecting natural emotions. Four adult English datasets covering acted, elicited and natural emotions are considered. A state-of-the-art model is proposed to accurately investigate the degradation of performance.
  arXiv  Detail & Related papers  (2022-07-05T15:15:22Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
  We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
  arXiv  Detail & Related papers  (2021-10-24T02:41:41Z)
• Stimuli-Aware Visual Emotion Analysis [75.68305830514007]
  We propose a stimuli-aware visual emotion analysis (VEA) method consisting of three stages, namely stimuli selection, feature extraction and emotion prediction. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets.
  arXiv  Detail & Related papers  (2021-09-04T08:14:52Z)
• Impact of multiple modalities on emotion recognition: investigation into 3d facial landmarks, action units, and physiological data [4.617405932149653]
  We analyze 3D facial data, action units, and physiological data as it relates to their impact on emotion recognition. Our analysis indicates that both 3D facial landmarks and physiological data are encouraging for expression/emotion recognition. On the other hand, while action units can positively impact emotion recognition when fused with other modalities, the results suggest it is difficult to detect emotion using them in a unimodal fashion.
  arXiv  Detail & Related papers  (2020-05-17T18:59:57Z)
• Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks [6.676572642463495]
  We propose a system that is able to recognize emotions with a high accuracy rate and in real time. In order to increase the accuracy of the recognition system, we analyze also the speech data and fuse the information coming from both sources.
  arXiv  Detail & Related papers  (2020-02-29T22:09:46Z)
• 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.