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Prosopagnosia01:24

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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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A Review of Machine Learning and Deep Learning Methods for Person Detection, Tracking and Identification, and Face

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Summary

This review analyzes face recognition, tracking, and person detection, noting advancements in deep learning methods. Key challenges remain in robustness, adaptability, and ethical considerations for these AI technologies.

Keywords:
computer visiondeep learningface recognitionperson detectionperson identificationperson trackingvideo analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Face recognition, tracking, and person detection are critical AI technologies.
  • Rapid advancements have been driven by machine learning and deep learning.
  • Existing research presents a fragmented view of current capabilities and limitations.

Purpose of the Study:

  • To comprehensively analyze recent developments in face recognition, tracking, identification, and person detection.
  • To highlight the benefits and drawbacks of current techniques.
  • To identify research gaps and trends in machine learning and deep learning applications.

Main Methods:

  • Systematic literature review using the PRISMA method.
  • Screening and evaluation of 142 relevant journal articles.
  • Analysis of methodological quality, reporting compliance, and sufficiency.

Main Results:

  • Identified a clear transition from classical to deep learning methods.
  • Detailed statistics on current trends and datasets for each task (detection, tracking, identification, recognition).
  • Highlighted significant improvements in performance metrics.

Conclusions:

  • Deep learning methods show notable improvements in face recognition and person detection.
  • Challenges persist in model robustness (lighting, occlusion), camera angle adaptation, and ethical/legal privacy issues.
  • Further research is needed to address these limitations for real-world deployment.