AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit

Bishay, Mina; Preston, Kenneth; Strafuss, Matthew; Page, Graham; Turcot, Jay; Mavadati, Mohammad

doi:10.1109/fg57933.2023.10042673

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…The ADFES accuracy of 80% is only third out of four accuracies, although it does outperform AFFDEX, a commercial algorithm. 92 Finally the accuracy of 64% gained from analysing the Oulu-Casia dataset is in the mid-range of other accuracies.…”

Section: Evaluation Of the Resultsmentioning

confidence: 88%

Facial emotion recognition with a reduced feature set for video game and metaverse avatars

Bellenger,

Chen,

2024

Computer Animation & Virtual

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This paper presents a novel real‐time facial feature extraction algorithm, producing a small feature set, suitable for implementing emotion recognition with online game and metaverse avatars. The algorithm aims to reduce data transmission and storage requirements, hurdles in the adoption of emotion recognition in these mediums. The early results presented show a facial emotion recognition accuracy of up to 92% on one benchmark dataset, with an overall accuracy of 77.2% across a wide range of datasets, demonstrating the early promise of the research.

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Section: Evaluation Of the Resultsmentioning

confidence: 88%

Facial emotion recognition with a reduced feature set for video game and metaverse avatars

Bellenger,

Chen,

2024

Computer Animation & Virtual

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“…While negative emotions are considered active, when the participant is lowering the brow and depressing the lip corner. Whereas neutral expression is, when all seven basic emotions (disgust, anger, joy, fear, surprise, sadness, and contempt) are absent [40].…”

Section: Facial Expression Analysis (Fea)mentioning

confidence: 99%

The Psychophysiological Interrelationship between Working Conditions and Stress of Harvester and Forwarder Drivers – a Study Protocol

Foisner,

Haas,

Göttlicher

et al. 2024

Preprint

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(1) Background: The proportion of fully mechanized harvesting systems in Austria is constantly increasing especially because of the technological development of the machines, which allows them to drive on increasingly steep terrain. This leads to additional challenges and potential hazards for the operators, which may also cause higher stress levels. The purpose of this study is to develop and debate a new set of methodologies for determining time and space resolved psychophysiology data to analyze stressful and non-stressful work situations for harvester and forwarder operators; (2) Methods: we suggest a study protocol to analyze a) tasks performed by the driver, b) environmental factors, like slope gradient, c) the psychophysiological response of experts during these tasks, and d) evaluate the worker´s subjective stress and fatigue before and after a harvest operation. (3) Results: Within this manuscript we present a study protocol based on quantitative and qualitative methods, like time and space resolved psychophysiology, questionnaires, qualitative interviews, as well as video and sensor-based analysis of environmental and machine data. The first results from real measurements of a forwarder driver are presented; (4) Conclusions: This multimodal research protocol serves to explore the relationship between stress, fatigue, and performed tasks. Through our quantitative and qualitative methodology, researchers can improve the health and safety of forest workers at work, improve their productivity and reduce damage to remaining trees.

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“…Affdex 2.0 [ 20 ]: Affdex 2.0 is a commercial software program designed to analyze facial behaviors in the wild. For face detection, it exploits region-based convolutional neural networks (R-CNNs [ 57 ]), which perform better on challenging conditions (e.g., variations in illumination, hand occlusions, etc.)…”

Section: Analyzing Naturalistic Facial Expressions With Deep Learningmentioning

confidence: 99%

“…The first section evaluates several newly developed and easy-to-use FER toolboxes for facial expression analysis in unconstrained environments. To support researchers in making informed decisions regarding the selection of appropriate toolboxes, we critically review the performance of five FER toolboxes, namely OpenFace 2.0 [ 19 ], Affdex 2.0 [ 20 ], Py-Feat [ 21 ], LibreFace [ 22 ], and PyAFAR [ 23 ], along with their underlying neural architectures and databases used for model training. The second section discusses the potential utilization of MLLMs for analyzing and interpreting naturalistic expressions in association with contextual cues.…”

Section: Introductionmentioning

confidence: 99%

Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models

Bian,

Küster,

Liu

et al. 2023

Sensors

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This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.

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AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit

Cited by 15 publications

References 42 publications

Facial emotion recognition with a reduced feature set for video game and metaverse avatars

Facial emotion recognition with a reduced feature set for video game and metaverse avatars

The Psychophysiological Interrelationship between Working Conditions and Stress of Harvester and Forwarder Drivers – a Study Protocol

Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models

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