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This study introduces a hybrid AI approach for robust person identification, even with face masks or poor lighting. It combines facial and gait recognition for high accuracy in challenging scenarios.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Facial recognition is vital for identity but challenged by masks and environmental factors.
  • The COVID-19 pandemic heightened the need for reliable identification despite face coverings.
  • Current person recognition systems have weaknesses in varied conditions like poor lighting or obscured faces.

Purpose of the Study:

  • To develop an advanced person identification system resilient to face masks and suboptimal conditions.
  • To enhance the accuracy and reliability of biometric identification using AI.
  • To overcome limitations in existing face and gait recognition technologies.

Main Methods:

  • A hybrid approach integrating deep and machine learning algorithms.
  • Development of models for detecting individuals with or without masks.
  • Incorporation of gait analysis as a complementary biometric identifier.

Main Results:

  • Achieved excellent performance in detecting faces and gait, with F1 scores, precision, and recall between 97% and 100%.
  • Demonstrated significantly higher recognition accuracy compared to baseline Convolutional Neural Network (CNN) systems.
  • Successfully addressed challenges posed by masked faces, poor lighting, and non-frontal views.

Conclusions:

  • The proposed hybrid AI model offers a superior solution for person identification in diverse and challenging environments.
  • This technology significantly improves upon existing methods for biometric recognition.
  • The system provides a robust and accurate method for identifying individuals, even when faces are partially or fully obscured.