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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Using Retinal Imaging to Study Dementia
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Detecting Dementia from Face-Related Features with Automated Computational Methods.

Chuheng Zheng1, Mondher Bouazizi2, Tomoaki Ohtsuki2

  • 1Graduate School of Science and Technology, Keio University, Yokohama 223-0061, Kanagawa, Japan.

Bioengineering (Basel, Switzerland)
|July 29, 2023
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Early Alzheimer's disease (AD) detection is crucial. This study shows face-related features, like HOG, can significantly aid in automated dementia screening, offering a less stressful alternative.

Keywords:
HOGaction unitdementia detectionface meshmachine learning

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Area of Science:

  • Computational neuroscience
  • Medical imaging analysis
  • Gerontology

Background:

  • Alzheimer's disease (AD) is a progressive dementia with no cure, necessitating early detection for timely intervention.
  • Current AD screening methods (brain scans, psychiatric tests) are costly and stressful, leading to patient reluctance.
  • Limited research has explored face-related features for dementia detection, despite advancements in language-based approaches.

Purpose of the Study:

  • To investigate the efficacy of face-related features in detecting Alzheimer's disease (AD).
  • To explore the potential of automated computational methods for dementia screening using video data.

Main Methods:

  • Utilized the PROMPT dataset containing video interviews of patients with dementia.
  • Extracted three types of facial features: face mesh, Histogram of Oriented Gradients (HOG), and Action Units (AU).
  • Trained and evaluated traditional and deep learning models on these extracted features for dementia detection.

Main Results:

  • Histogram of Oriented Gradients (HOG) features achieved the highest accuracy (79%) in dementia detection.
  • Action Unit (AU) features demonstrated 71% accuracy, while face mesh features yielded 66% accuracy.
  • The study highlights the effectiveness of HOG and AU features over face mesh for AD detection.

Conclusions:

  • Face-related features show significant potential as crucial indicators for automated computational dementia detection.
  • HOG features offer a promising avenue for developing accurate and accessible AD screening tools.
  • Further research into facial feature analysis can lead to less invasive and more effective dementia detection strategies.