most citedAdvancing Face-to-Face Emotion Communication: A Multimodal Dataset (AFFEC)

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

cs.HC2025

Modelling the Interplay of Eye-Tracking Temporal Dynamics and Personality for Emotion Detection in Face-to-Face Settings

Meisam J. Seikavandi, Jostein Fimland, Fabricio Batista Narcizo +5

Accurate recognition of human emotions is critical for adaptive human-computer interaction, yet remains challenging in dynamic, conversation-like settings. This work presents a per…

cs.HC2025

MuMTAffect: A Multimodal Multitask Affective Framework for Personality and Emotion Recognition from Physiological Signals

Meisam Jamshidi Seikavandi, Fabricio Batista Narcizo, Ted Vucurevich +2

We present MuMTAffect, a novel Multimodal Multitask Affective Embedding Network designed for joint emotion classification and personality prediction (re-identification) from short…

cs.HC20251 cited

Advancing Face-to-Face Emotion Communication: A Multimodal Dataset (AFFEC)

Meisam J. Sekiavandi, Laurits Dixen, Jostein Fimland +5

Emotion recognition has the potential to play a pivotal role in enhancing human-computer interaction by enabling systems to accurately interpret and respond to human affect. Yet, c…

cs.HC2025

Exploring the Temporal Dynamics of Facial Mimicry in Emotion Processing Using Action Units

Meisam Jamshidi Seikavandi, Jostein Fimland, Maria Jung Barrett +1

Facial mimicry - the automatic, unconscious imitation of others' expressions - is vital for emotional understanding. This study investigates how mimicry differs across emotions usi…

cs.HC2025

Modeling Face Emotion Perception from Naturalistic Face Viewing: Insights from Fixational Events and Gaze Strategies

Meisam J. Seikavandi, Maria J. Barrett, Paolo Burelli

Face Emotion Recognition (FER) is essential for social interactions and understanding others' mental states. Utilizing eye tracking to investigate FER has yielded insights into cog…

cs.AI2025

Don't Get Too Excited -- Eliciting Emotions in LLMs

Gino Franco Fazzi, Julie Skoven Hinge, Stefan Heinrich +1

This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogue…