SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913083994144768 |
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| author | Taati, Babak Muzammil, Muhammad Zarghami, Yasamin Moturu, Abhishek Kazerouni, Amirhossein Reimer, Hailey Mihailidis, Alex Hadjistavropoulos, Thomas |
| author_facet | Taati, Babak Muzammil, Muhammad Zarghami, Yasamin Moturu, Abhishek Kazerouni, Amirhossein Reimer, Hailey Mihailidis, Alex Hadjistavropoulos, Thomas |
| contents | Accurate pain assessment in patients with limited ability to communicate, such as older adults with severe dementia, represents a critical healthcare challenge. Robust automated systems of pain behavior detection may facilitate such assessments. Existing pain detection datasets, however, suffer from limited ethnic/racial diversity, privacy constraints, and underrepresentation of older adults who are the primary target population for clinical deployment. We present SynPAIN, a large-scale synthetic dataset containing 10,710 facial expression images across five ethnicities/races, representing two age groups, and two genders. Using commercial generative AI tools, we created demographically balanced synthetic identities with clinically meaningful pain expressions. Our validation demonstrates that synthetic pain expressions exhibit expected pain patterns, scoring significantly higher than neutral and non-pain expressions using clinically validated pain assessment tools based on facial action unit analysis. We experimentally demonstrate SynPAIN's utility in identifying algorithmic bias in existing pain detection models. Through comprehensive bias evaluation, we reveal substantial performance disparities across demographics characteristics. These performance disparities were previously undetectable with smaller, less diverse datasets. Furthermore, we demonstrate that age-matched synthetic data augmentation improves pain detection performance on real clinical data, achieving a 2.4 percentage point improvement in average precision. SynPAIN addresses critical gaps in pain assessment research by providing the first publicly available, demographically diverse synthetic dataset specifically designed for older adult pain detection, while establishing a framework for measuring and mitigating algorithmic bias. The dataset, code, and trained models is available at https://mmzml.github.io/SynPAIN |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_19673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions Taati, Babak Muzammil, Muhammad Zarghami, Yasamin Moturu, Abhishek Kazerouni, Amirhossein Reimer, Hailey Mihailidis, Alex Hadjistavropoulos, Thomas Computer Vision and Pattern Recognition Accurate pain assessment in patients with limited ability to communicate, such as older adults with severe dementia, represents a critical healthcare challenge. Robust automated systems of pain behavior detection may facilitate such assessments. Existing pain detection datasets, however, suffer from limited ethnic/racial diversity, privacy constraints, and underrepresentation of older adults who are the primary target population for clinical deployment. We present SynPAIN, a large-scale synthetic dataset containing 10,710 facial expression images across five ethnicities/races, representing two age groups, and two genders. Using commercial generative AI tools, we created demographically balanced synthetic identities with clinically meaningful pain expressions. Our validation demonstrates that synthetic pain expressions exhibit expected pain patterns, scoring significantly higher than neutral and non-pain expressions using clinically validated pain assessment tools based on facial action unit analysis. We experimentally demonstrate SynPAIN's utility in identifying algorithmic bias in existing pain detection models. Through comprehensive bias evaluation, we reveal substantial performance disparities across demographics characteristics. These performance disparities were previously undetectable with smaller, less diverse datasets. Furthermore, we demonstrate that age-matched synthetic data augmentation improves pain detection performance on real clinical data, achieving a 2.4 percentage point improvement in average precision. SynPAIN addresses critical gaps in pain assessment research by providing the first publicly available, demographically diverse synthetic dataset specifically designed for older adult pain detection, while establishing a framework for measuring and mitigating algorithmic bias. The dataset, code, and trained models is available at https://mmzml.github.io/SynPAIN |
| title | SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.19673 |