Configural Representation of Facial Action Units for Spontaneous Facial Expression Recognition in the Wild

Perveen, Nazil; Mohan, C. Krishna

doi:10.5220/0009099700930102

Cited by 8 publications

(7 citation statements)

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“…But a number were found to either not to predict in real-time, or be based on having an initial neutral image of a subject as a reference point. 64,65…”

Section: Proposed Reduced Feature Set and Feature Extractormentioning

confidence: 99%

“…In making the decision to design a simpler set of calculations, research was undertaken into other algorithms that predicted emotion without using transient features. But a number were found to either not to predict in real‐time, or be based on having an initial neutral image of a subject as a reference point 64,65 …”

Section: Proposed Reduced Feature Set and Feature Extractormentioning

confidence: 99%

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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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“…But a number were found to either not to predict in real-time, or be based on having an initial neutral image of a subject as a reference point. 64,65…”

Section: Proposed Reduced Feature Set and Feature Extractormentioning

confidence: 99%

Section: Proposed Reduced Feature Set and Feature Extractormentioning

confidence: 99%

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

Bellenger,

Chen,

2024

Computer Animation & Virtual

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show abstract

“…Given a dataset of 2D face images, AUFART is trained by minimizing: matrix , and 2D transition , respectively. We employ the Mediapipe landmark detector to predict landmarks from 2D images, utilizing a total of 105 landmarks distributed across the eyebrows, eyes, nose, and mouth regions [27]. Table 1 provides details on the facial landmarks associated with AUs, and Fig.…”

Section: Loss Functionmentioning

confidence: 99%

“…The AU-based relative distance loss computes the relative distance between AU con gural features for image landmarks and the projected 3D landmarks. The AU con gural features involve calculating relative distances between facial landmark points and are used to determine AUs [27]. For example, AU 4 (Brow Lowerer) is determined based on the distance between the landmark points 21 and 22, which correspond to the inner eyebrow landmarks on the left and right.…”

Section: Loss Functionmentioning

confidence: 99%

Action Unit-Based 3D Face Reconstruction Using Transformers

Kim,

Wang,

Lee

2024

Preprint

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The reconstruction of 3D face shapes and expressions from single 2D images remains unconquered due to the lack of detailed modeling of human facial movements such as the correlation between the different parts of faces. Facial action units (AUs), which represent detailed taxonomy of the human facial movements based on observation of activation of muscles or muscle groups, can be used to model various facial expression types. We present a novel 3D face reconstruction framework called AU feature-based 3D FAce Reconstruction using Transformer (AUFART) that can generate a 3D face model that is responsive to AU activation given a single monocular 2D image to capture expressions. AUFART leverages AU-specific features as well as facial global features to achieve accurate 3D reconstruction of facial expressions using transformers. We also introduce a loss function which is to force the learning toward the minimal discrepancy in AU activations between the input and rendered reconstruction. The proposed framework achieves an average F1 score of 0.39, outperforming state-of-the-art methods.

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“…In making the decision to design a simpler set of calculations, research was undertaken into other algorithms that predicted emotion without using transient features. But a number were found to either not to predict in real-time, or be based on having an initial neutral image of a subject as a reference point [91,92].…”

Section: Proposed Reduced Feature Set and Feature Extractormentioning

confidence: 99%

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

Bellenger

Chen

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Configural Representation of Facial Action Units for Spontaneous Facial Expression Recognition in the Wild

Cited by 8 publications

References 0 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

Action Unit-Based 3D Face Reconstruction Using Transformers

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

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