Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia
The need for the automated facial expression analysis arises in various clinical settings involving mental and physical health assessment of older adults. However, the effect of age (young versus old) and ability (healthy versus physical or cognitive impairment) on the performance of available metho...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8643365/ |
| _version_ | 1818416036354981888 |
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| author | Babak Taati Shun Zhao Ahmed B. Ashraf Azin Asgarian M. Erin Browne Kenneth M. Prkachin Alex Mihailidis Thomas Hadjistavropoulos |
| author_facet | Babak Taati Shun Zhao Ahmed B. Ashraf Azin Asgarian M. Erin Browne Kenneth M. Prkachin Alex Mihailidis Thomas Hadjistavropoulos |
| author_sort | Babak Taati |
| collection | DOAJ |
| description | The need for the automated facial expression analysis arises in various clinical settings involving mental and physical health assessment of older adults. However, the effect of age (young versus old) and ability (healthy versus physical or cognitive impairment) on the performance of available methods has not yet been investigated. In this paper, we demonstrate a bias affecting the performance of common facial landmark detection and expression recognition algorithms on the faces of older adults with dementia. We also investigate the ways of mitigating this bias via the addition of representative training examples. Results show that landmark placement is less accurate when tested on the faces of individuals with dementia as compared to older adults who are cognitively healthy. Retraining or fine-tuning the methods with images of older adults' faces improves the performance significantly, but the gap between older adults with versus without dementia persists. As the interest in using facial analysis methods in clinical applications grows, results of this study: 1) highlight the limitations of the existing models when applied to clinical populations and 2) shed light on methods of addressing these limitations as well as the need to develop algorithms designed to be fair with respect to variables such as age and ability. |
| first_indexed | 2024-12-14T11:44:30Z |
| format | Article |
| id | doaj.art-7724e31ef83b4b5282a1f82ea69de3a7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:44:30Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7724e31ef83b4b5282a1f82ea69de3a72022-12-21T23:02:39ZengIEEEIEEE Access2169-35362019-01-017255272553410.1109/ACCESS.2019.29000228643365Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With DementiaBabak Taati0https://orcid.org/0000-0001-9763-4293Shun Zhao1Ahmed B. Ashraf2Azin Asgarian3M. Erin Browne4Kenneth M. Prkachin5Alex Mihailidis6Thomas Hadjistavropoulos7Toronto Rehabilitation Institute, University Health Network, Toronto, CanadaToronto Rehabilitation Institute, University Health Network, Toronto, CanadaToronto Rehabilitation Institute, University Health Network, Toronto, CanadaToronto Rehabilitation Institute, University Health Network, Toronto, CanadaDepartment of Psychology, University of Regina, Regina, CanadaDepartment of Psychology, University of Northern British Columbia, Prince George, CanadaToronto Rehabilitation Institute, University Health Network, Toronto, CanadaDepartment of Psychology, University of Regina, Regina, CanadaThe need for the automated facial expression analysis arises in various clinical settings involving mental and physical health assessment of older adults. However, the effect of age (young versus old) and ability (healthy versus physical or cognitive impairment) on the performance of available methods has not yet been investigated. In this paper, we demonstrate a bias affecting the performance of common facial landmark detection and expression recognition algorithms on the faces of older adults with dementia. We also investigate the ways of mitigating this bias via the addition of representative training examples. Results show that landmark placement is less accurate when tested on the faces of individuals with dementia as compared to older adults who are cognitively healthy. Retraining or fine-tuning the methods with images of older adults' faces improves the performance significantly, but the gap between older adults with versus without dementia persists. As the interest in using facial analysis methods in clinical applications grows, results of this study: 1) highlight the limitations of the existing models when applied to clinical populations and 2) shed light on methods of addressing these limitations as well as the need to develop algorithms designed to be fair with respect to variables such as age and ability.https://ieeexplore.ieee.org/document/8643365/Facial analysisolder adultsdementiafacial landmark detectionfacial action units |
| spellingShingle | Babak Taati Shun Zhao Ahmed B. Ashraf Azin Asgarian M. Erin Browne Kenneth M. Prkachin Alex Mihailidis Thomas Hadjistavropoulos Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia IEEE Access Facial analysis older adults dementia facial landmark detection facial action units |
| title | Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia |
| title_full | Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia |
| title_fullStr | Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia |
| title_full_unstemmed | Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia |
| title_short | Algorithmic Bias in Clinical Populations—Evaluating and Improving Facial Analysis Technology in Older Adults With Dementia |
| title_sort | algorithmic bias in clinical populations x2014 evaluating and improving facial analysis technology in older adults with dementia |
| topic | Facial analysis older adults dementia facial landmark detection facial action units |
| url | https://ieeexplore.ieee.org/document/8643365/ |
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