3.8 Proceedings Paper

Privileged Information for Modeling Affect In The Wild

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games

Konstantinos Makantasis et al.

Summary: This article investigates the possibility of predicting emotions solely based on audiovisual information in videos using deep learned representations. The study shows that general-purpose representations can be built to detect emotions by relying only on the pixels and audio of a human-computer interaction video, as demonstrated in the domain of digital games.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2023)

Article Engineering, Electrical & Electronic

Advancing Remote Healthcare Using Humanoid and Affective Systems

Utkarsh Tripathi et al.

Summary: This paper presents a solution that combines a brain-computer interface system and a humanoid robot for remote teaching. Using Kinect and deep learning algorithms, the system can understand the emotional state of the trainee and enable real-time communication through the humanoid robot.

IEEE SENSORS JOURNAL (2022)

Proceedings Paper Computer Science, Artificial Intelligence

DETECTING EXPRESSIONS WITH MULTIMODAL TRANSFORMERS

Srinivas Parthasarathy et al.

Summary: Developing machine learning algorithms for audio-visual expression detection shows promising results compared to baseline models, with the proposed methods achieving better performance on the Aff-Wild2 database. Additionally, multimodal architectures demonstrate significant improvements over models trained on single modality.

2021 IEEE SPOKEN LANGUAGE TECHNOLOGY WORKSHOP (SLT) (2021)

Article Computer Science, Artificial Intelligence

Estimation of continuous valence and arousal levels from faces in naturalistic conditions

Antoine Toisoul et al.

Summary: Facial affect analysis aims to enable computers to better understand a person's emotional state through a novel deep neural network architecture. While estimating valence and arousal from a face is natural for humans, it is challenging for computer-based systems. Attention must be paid to potential ethical issues when using this tool.

NATURE MACHINE INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond

Dimitrios Kollias et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2019)

Article Computer Science, Artificial Intelligence

Improving Driver Emotions with Affective Strategies

Michael Braun et al.

MULTIMODAL TECHNOLOGIES AND INTERACTION (2019)

Article Computer Science, Artificial Intelligence

AFEW-VA database for valence and arousal estimation in-the-wild

Jean Kossaifi et al.

IMAGE AND VISION COMPUTING (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Aff-Wild: Valence and Arousal 'in-the-wild' Challenge

Stefanos Zafeiriou et al.

2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Recognition of Affect in the wild using Deep Neural Networks

Dimitrios Kollias et al.

2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

Adrian Bulat et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

Proceedings Paper Computer Science, Theory & Methods

Deep Learning for Emotion Recognition on Small Datasets Using Transfer Learning

Hong-Wei Ng et al.

ICMI'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (2015)

Article Hospitality, Leisure, Sport & Tourism

Measuring Emotions in Real Time: Implications for Tourism Experience Design

Jeongmi (Jamie) Kim et al.

JOURNAL OF TRAVEL RESEARCH (2015)

Article Computer Science, Artificial Intelligence

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

Xing Zhang et al.

IMAGE AND VISION COMPUTING (2014)

Proceedings Paper Computer Science, Artificial Intelligence

Real-time Mobile Facial Expression Recognition System - A Case Study

Myunghoon Suk et al.

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2014)

Article Computer Science, Artificial Intelligence

DISFA: A Spontaneous Facial Action Intensity Database

S. Mohammad Mavadati et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2013)

Article Computer Science, Information Systems

Finding Celebrities in Billions of Web Images

Xiao Zhang et al.

IEEE TRANSACTIONS ON MULTIMEDIA (2012)

Article Computer Science, Artificial Intelligence

DEAP: A Database for Emotion Analysis Using Physiological Signals

Sander Koelstra et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2012)

Article Computer Science, Cybernetics

Towards affective camera control in games

Georgios N. Yannakakis et al.

USER MODELING AND USER-ADAPTED INTERACTION (2010)

Review Computer Science, Artificial Intelligence

Emotion on the Road-Necessity, Acceptance, and Feasibility of Affective Computing in the Car

Florian Eyben et al.

ADVANCES IN HUMAN-COMPUTER INTERACTION (2010)

Article Computer Science, Artificial Intelligence

A new learning paradigm: Learning using privileged information

Vladimir Vapnik et al.

NEURAL NETWORKS (2009)