Publication

Distribution-based iterative pairwise classification of emotions in the wild using LGBP-TOP

Related publications (34)

Compassion and Engineering Students’ Moral Reasoning: The Emotional Experience of Engineering Ethics Cases

Background: There has been an increase in interest in emotion in engineering and science ethics education. There is also evidence that emotional content in case studies may improve students’ learning and enhance awareness, understanding, and motivation con ...

2023

Towards Empathetic Open-Domain Chatbots

Yubo Xie

The research community of dialog generation has been interested in incorporating emotional information into the design of open-domain dialog systems ever since neural networks (sequence-to-sequence models in particular) were adopted for modeling dialogs. T ...

EPFL2022

Emotion Recognition in a Multi-Componential Framework: The Role of Physiology

Maëlan Quentin Menétrey, Gelareh Mohammadi

The Component Process Model is a well-established framework describing an emotion as a dynamic process with five highly interrelated components: cognitive appraisal, expression, motivation, physiology and feeling. Yet, few empirical studies have systematic ...

FRONTIERS MEDIA SA2022

Emotion Recognition in a Multi-Componential Framework: The Role of Physiology

Gelareh Mohammadi

Emotions are rich and complex experiences involving various behavioral and physiological responses. While many empirical studies have focused on discrete and dimensional representations of emotions, these representations do not fully reconcile with recent ...

FRONTIERS MEDIA SA2022

A Novel Framework for Assessment of Deep Face Recognition Systems in Realistic Conditions

Touradj Ebrahimi, Yuhang Lu, Luca Barras

Deep convolutional neural networks have shown remarkable results on face recognition (FR). Despite their significant progress, the performance of current face recognition techniques is often assessed in benchmarks under not always realistic conditions. The ...

2022

Towards Recognizing Emotion in the Latent Space

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Emotion recognition is usually achieved by collecting features (physiological signals, events, facial expressions, etc.) to predict an emotional ground truth. This ground truth is arguably unreliable due to its subjective nature. In this paper, we introduc ...

IEEE2021

Dynamic functional brain networks underlying the temporal inertia of negative emotions

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Gwladys Rey, Julian Gaviria

Affective inertia represents the lasting impact of transient emotions at one time point on affective state at a subsequent time point. Here we describe the neural underpinnings of inertia following negative emotions elicited by sad events in movies. Using ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

Multi-Modal Recurrent Attention Networks for Facial Expression Recognition

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Recent deep neural networks based methods have achieved state-of-the-art performance on various facial expression recognition tasks. Despite such progress, previous researches for facial expression recognition have mainly focused on analyzing color recordi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Context-Aware Emotion Recognition Networks

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Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion ...

IEEE2019

G2-VER: Geometry Guided Model Ensemble for Video-based Facial Expression Recognition

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Hazim Kemal Ekenel, Tanguy Albrici

This paper addresses the problem of automatic facial expression recognition in videos, where the goal is to predict discrete emotion labels best describing the emotions expressed in short video clips. Building on a pre-trained convolutional neural network ...

IEEE2019

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