Facial Action Unit Detection using 3D Face Landmarks for Pain Detection
Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in research can be challenging since they are typically manually annotated, which can be time-consuming, repetitive, and error-prone. Advancements in...
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| Veröffentlicht in: | 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Jg. 2023; S. 1 - 5 |
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IEEE
24.07.2023
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| ISSN: | 2694-0604, 2694-0604 |
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| Abstract | Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in research can be challenging since they are typically manually annotated, which can be time-consuming, repetitive, and error-prone. Advancements in automated AU detection can greatly reduce the time required for this task and improve the reliability of annotations for downstream tasks, such as pain detection. In this study, we present an efficient method for detecting AUs using only 3D face landmarks. Using the detected AUs, we trained state-of-the-art deep learning models to detect pain, which validates the effectiveness of the AU detection model. Our study also establishes a new benchmark for pain detection on the BP4D+ dataset, demonstrating an 11.13% improvement in F1-score and a 3.09% improvement in accuracy using a Transformer model compared to existing studies. Our results show that utilizing only eight predicted AUs still achieves competitive results when compared to using all 34 ground-truth AUs. |
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| AbstractList | Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in research can be challenging since they are typically manually annotated, which can be time-consuming, repetitive, and error-prone. Advancements in automated AU detection can greatly reduce the time required for this task and improve the reliability of annotations for downstream tasks, such as pain detection. In this study, we present an efficient method for detecting AUs using only 3D face landmarks. Using the detected AUs, we trained state-of-the-art deep learning models to detect pain, which validates the effectiveness of the AU detection model. Our study also establishes a new benchmark for pain detection on the BP4D+ dataset, demonstrating an 11.13% improvement in F1-score and a 3.09% improvement in accuracy using a Transformer model compared to existing studies. Our results show that utilizing only eight predicted AUs still achieves competitive results when compared to using all 34 ground-truth AUs. Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in research can be challenging since they are typically manually annotated, which can be time-consuming, repetitive, and error-prone. Advancements in automated AU detection can greatly reduce the time required for this task and improve the reliability of annotations for downstream tasks, such as pain detection. In this study, we present an efficient method for detecting AUs using only 3D face landmarks. Using the detected AUs, we trained state-of-the-art deep learning models to detect pain, which validates the effectiveness of the AU detection model. Our study also establishes a new benchmark for pain detection on the BP4D+ dataset, demonstrating an 11.13% improvement in F1-score and a 3.09% improvement in accuracy using a Transformer model compared to existing studies. Our results show that utilizing only eight predicted AUs still achieves competitive results when compared to using all 34 ground-truth AUs.Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in research can be challenging since they are typically manually annotated, which can be time-consuming, repetitive, and error-prone. Advancements in automated AU detection can greatly reduce the time required for this task and improve the reliability of annotations for downstream tasks, such as pain detection. In this study, we present an efficient method for detecting AUs using only 3D face landmarks. Using the detected AUs, we trained state-of-the-art deep learning models to detect pain, which validates the effectiveness of the AU detection model. Our study also establishes a new benchmark for pain detection on the BP4D+ dataset, demonstrating an 11.13% improvement in F1-score and a 3.09% improvement in accuracy using a Transformer model compared to existing studies. Our results show that utilizing only eight predicted AUs still achieves competitive results when compared to using all 34 ground-truth AUs. |
| Author | Maia, Deise Santana Feghoul, Kevin Bouazizi, Mondher |
| Author_xml | – sequence: 1 givenname: Kevin surname: Feghoul fullname: Feghoul, Kevin email: kevin.feghoul@univ-lille.fr organization: University of Lille,Inserm, CHU Lille, UMRS1172 - LilNCog, UMR 9189 CRIStAL,Lille,France,F-59000 – sequence: 2 givenname: Mondher surname: Bouazizi fullname: Bouazizi, Mondher email: mondher.bouazizi@keio.jp organization: Keio University,Faculty of Science and Technology,Japan – sequence: 3 givenname: Deise Santana surname: Maia fullname: Maia, Deise Santana email: deise.santanamaia@univ-lille.fr organization: University of Lille,CNRS, Centrale Lille, UMR 9189 CRIStAL,Lille,France,F-59000 |
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| Snippet | Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in... |
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| SubjectTerms | Benchmark testing Benchmarking Biological system modeling Deep learning Face Facial Expression Gold Humans Pain Pain - diagnosis Reproducibility of Results Three-dimensional displays Transformers |
| Title | Facial Action Unit Detection using 3D Face Landmarks for Pain Detection |
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