Real-Time Surveillance System based on Facial Recognition using YOLOv5

Facial recognition using deep learning techniques is now a rapidly growing and widely applied aspect of real-time surveillance systems with broad range of applications in every field. Recognizing multiple faces in real-time is very challenging due to adverse environmental conditions and occlusion ef...

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Bibliographic Details
Published in2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) pp. 1 - 6
Main Authors Majeed, Fahad, Khan, Farrukh Zeeshan, Iqbal, Muhammad Javed, Nazir, Maria
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.07.2021
Subjects
Computer architecture
Deep learning
Face recognition
Feeds
occlusion effects
Real-time systems
Streaming media
Surveillance
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Summary:Facial recognition using deep learning techniques is now a rapidly growing and widely applied aspect of real-time surveillance systems with broad range of applications in every field. Recognizing multiple faces in real-time is very challenging due to adverse environmental conditions and occlusion effects. YOLOv5 is the current state-of-the-art algorithm for real-time facial recognition with very limited experimental analysis. In this paper YOLOv5 has been trained from scratch and tested on FDDB and customized dataset from real-time video feed. Experiments show 87% accuracy on FDDB while 94% accuracy on the customized dataset. The paper also presents comparative analysis of the results with the previous versions of YOLOv5 (YOLOv3 and YOLOv4). The algorithm is also tested on real-time environment and has the capability to detect multiple faces with maximum accuracy.
DOI:10.1109/MAJICC53071.2021.9526254