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
Citations
IRE Journals:
Chendo Chukwunonso Nnamdi , Okeke Ogochukwu , Mgbeafulike Ike , Okafor Patrick
"Real-Time Face Recognition with Occlusion Handling Using Facenet512 and Machine Learning" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 526-534
IEEE:
Chendo Chukwunonso Nnamdi , Okeke Ogochukwu , Mgbeafulike Ike , Okafor Patrick
"Real-Time Face Recognition with Occlusion Handling Using Facenet512 and Machine Learning" Iconic Research And Engineering Journals, 8(10)
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