Real-Time Face Recognition with Occlusion Handling Using Facenet512 and Machine Learning

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@article{1707891,
      author = {Chendo Chukwunonso Nnamdi, Okeke Ogochukwu, Mgbeafulike Ike, Okafor Patrick},
      title = {Real-Time Face Recognition with Occlusion Handling Using Facenet512 and Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {526-534},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707891.pdf},
      abstract = {This paper introduces a face recognition system designed to tackle the issue of occlusion, which can hinder the effectiveness of traditional facial recognition systems when parts of a face are obstructed. To address this, the system is built using object-oriented analysis and Design Methodology (OODAM), which promotes a modular and flexible development approach. The implementation is carried out in Python, utilizing the Deep Face library for face recognition.in particular, the system employs the ?Facenet512? Model with the ?Euclidean_12? distance metric to enhance accuracy in face identification, even in the presence of partial occlusions. Machine learning algorithms are integrated for feature extraction and matching, with a SQLite database used for storing and managing face efficiently. The architecture supports real-time detection and recognition of faces through OpenCV, while a kernelized correlation filters (KCF) tracker is used to ensure continuous tracking across video frames. The system also processes frames, addresses occlusions, and stores recognition outcomes in a well-organized database. Evaluation result highlights the system?s capacity to effectively mitigate the impact of occlusions, achieving improved recognition accuracy and reliability over traditional methods.},
      keywords = {Machine Learning, Face Recognition, Occlusion, Real-time Detection},
      month = {April},
  }