Emotion recognition in the wild

Dhall, Abhinav; Goecke, Roland; Gedeon, Tom; Sebe, Nicu

doi:10.1007/s12193-016-0213-z

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…Hence, the limitations listed in the preceding paragraphs Pose is defined in equation ( 6) and is given in degrees in the x-axis. The y-axis is the final score given by equation (3).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have reported good results in the automatic analysis of facial expressions of emotion [2]. However, these results were obtained with the analysis of images and videos taken in the laboratory [23,3]. That is, even when the expressions were spontaneous, the filming was done in controlled conditions with the full awareness of the participants.…”

Section: Introductionmentioning

confidence: 99%

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EmotioNet Challenge: Recognition of facial expressions of emotion in the wild

Benitez-Quiroz¹,

Srinivasan²,

Feng³

et al. 2017

Preprint

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This paper details the methodology and results of the EmotioNet challenge. This challenge is the first to test the ability of computer vision algorithms in the automatic analysis of a large number of images of facial expressions of emotion in the wild. The challenge was divided into two tracks. The first track tested the ability of current computer vision algorithms in the automatic detection of action units (AUs). Specifically, we tested the detection of 11 AUs. The second track tested the algorithms' ability to recognize emotion categories in images of facial expressions. Specifically, we tested the recognition of 16 basic and compound emotion categories. The results of the challenge suggest that current computer vision and machine learning algorithms are unable to reliably solve these two tasks. The limitations of current algorithms are more apparent when trying to recognize emotion. We also show that current algorithms are not affected by mild resolution changes, small occluders, gender or age, but that 3D pose is a major limiting factor on performance. We provide an in-depth discussion of the points that need special attention moving forward.

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“…Hence, the limitations listed in the preceding paragraphs Pose is defined in equation ( 6) and is given in degrees in the x-axis. The y-axis is the final score given by equation (3).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EmotioNet Challenge: Recognition of facial expressions of emotion in the wild

Benitez-Quiroz¹,

Srinivasan²,

Feng³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…However, emotion classification in uncontrolled, wild scenarios still remains a challenge due to the high variability arising from subject pose, environment illumination, and camera resolution. To this end, the literature suggests to enrich facial expression datasets by labeling captured images in different conditions (i.e., captured in real-world conditions and not in a laboratory) and to train models to overcome limitations of available algorithms in emotion recognition [41]; unfortunately, this procedure is still complex and costly.…”

Section: Facial Expression and Emotion Recognitionmentioning

confidence: 99%

On Driver Behavior Recognition for Increased Safety: A Roadmap

Davoli

Martalò

Cilfone

et al. 2020

Safety

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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“…Over the past two decades automatic emotion recognition has made significant progress. One of the largest challenges besetting automatic facial emotion recognition algorithms is that the developed frameworks have only been trained on data collected in controlled laboratory settings with frontal faces, perfect illumination and posed expressions [8]. These algorithms are then applied to images and videos from the internet which have been captured in different unconstrained environments and the algorithms, unsurprisingly, do not perform as well.…”

Section: Introductionmentioning

confidence: 99%

Valence/Arousal Estimation of Occluded Faces from VR Headsets

Gotsman

Polydorou

Edalat

2021

2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI)

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Emotion recognition from facial visual signals is a challenge which has attracted enormous interest over the past two decades. Researchers are attempting to teach computers to better understand a person's emotional state. Providing emotion recognition can massively enrich experiences. The benefits of this research for human-computer interactions are limitless. Emotions are intricate, and so we need a representative model of the full spectrum displayed by humans. A multi-dimensional emotion representation, which includes valence (how positive an emotion) and arousal (how calming or exciting an emotion), is a good fit. Virtual Reality (VR), a fully immersive computergenerated world, has witnessed significant growth over the past years. It has a wide range of applications including in mental health, such as exposure therapy and the self-attachment technique. In this paper, we address the problem of emotion recognition when the user is immersed in VR. Understanding emotions from facial cues is in itself a demanding task. It is made even harder when a head-mounted VR headset is worn, as now an occlusion blocks the upper half of the face. We attempt to overcome this issue by introducing EmoFAN-VR, a deep neural network architecture, to analyse facial affect in the presence of a severe occlusion from a VR headset with a high level of accuracy. We simulate an occlusion representing a VR headset and apply it to all datasets in this work. EmoFAN-VR predicts both discrete and continuous emotions in one step, meaning it can be used in real-time deployment. We fine-tune our network on the AffectNet dataset under VR occlusion and test it on the AFEW-VA dataset, setting a new baseline for this dataset whilst under VR occlusion.

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Emotion recognition in the wild

Cited by 16 publications

References 18 publications

EmotioNet Challenge: Recognition of facial expressions of emotion in the wild

EmotioNet Challenge: Recognition of facial expressions of emotion in the wild

On Driver Behavior Recognition for Increased Safety: A Roadmap

Valence/Arousal Estimation of Occluded Faces from VR Headsets

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