Face Mesh for Dementia Detection: Evaluating Data Augmentation in Deep Learning and Traditional Machine Learning

Topic: Risk, prevention and care in population studies

Alzheimer’s disease (AD) stands as a prevalent form of dementia, primarily impacting the elderly, and presently lacking any known cure. Early screening and intervention have demonstrated efficacy for both current and prospective treatments. With the global population aging, early detection of AD has become imperative. Language-based approaches for dementia detection have received considerable attention in research due to dementia’s nature of hurting cognitive and language functions. Facial features, on the other hand, have been comparatively neglected despite their potential in identifying AD. The face mesh, comprising 478 landmarks, serves as a rich and informative facial representation. This study concentrates on leveraging face mesh data extracted from the PROMPT dataset for AD detection. The dataset comprises 445 voice and video recordings from 117 dementia patients and healthy individuals collected during professionally conducted interview sessions. We explored data augmentation techniques to address the challenges of over-fitting, particularly impactful in training neural networks on a small dataset. Deep learning methods, including transformer, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were employed to classify dementia patients and healthy subjects. By augmenting the dataset, we improved the performance of the deep learning methods. The combined use of 1D CNN networks and data augmentation method reported the highest accuracy of 76%, which is the state-of-the-art accuracy in dementia detection using face mesh. These results confirmed the effectiveness of our data augmentation methods. They also underscore the potential of face-related features in enhancing early detection approaches for AD.

Key findings

• Early screening and intervention have demonstrated efficacy for both current and prospective treatments.
• These results confirmed the effectiveness of our data augmentation methods.

Why it matters

This study used face mesh data from video recordings to classify dementia patients versus healthy subjects, and found that data augmentation improved deep learning performance. It is about detection and screening, not treating, curing or preventing Alzheimer's disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1109/access.2025.3579241
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• Free full text: IEEE Access →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{zheng2025-3d70e527,
  author  = {Zheng, Chuheng and Bouazizi, Mondher and Okunishi, Taichi and Ohtsuki, Tomoaki Otsuki and Kitazawa, Momoko and Horigome, Toshiro and Kishimoto, Taishiro},
  title   = {{Face Mesh for Dementia Detection: Evaluating Data Augmentation in Deep Learning and Traditional Machine Learning}},
  journal = {IEEE Access},
  year    = {2025},
  doi     = {10.1109/access.2025.3579241},
  url     = {https://doi.org/10.1109/access.2025.3579241}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Prevalence of depression, anxiety, and apathy symptoms across dementia stages: A systematic review and meta-analysis International journal of geriatric psychiatry · 2021-05-04 · usefulness score 70/100 · Caregiving
• Monetary Costs of Dementia in the United States New England Journal of Medicine · 2013-04-04 · usefulness score 66/100 · Costs and economics
• A systematic literature review of automatic Alzheimer's disease detection from speech and language Journal of the American Medical Informatics Association : JAMIA · 2020-11-01 · usefulness score 60/100 · Digital health tools
• Screening of Alzheimer's disease by facial complexion using artificial intelligence Aging · 2021-01-25 · usefulness score 54/100 · Digital health tools
• Behavioral and psychological symptoms of dementia Frontiers in neurology · 2012-05-07 · usefulness score 52/100 · Risk factors

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