Author(s):
1. Romayyah:
Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture,Peshawar,Pakistan
2. Imran Uddin:
Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture,Peshawar,Pakistan
Abstract:
Face recognition systems require high accuracy and efficiency in real time. The study presents a comparative analysis of yolov5, a state-of-theart object detection model, against the traditional face recognition models like CNN and FaceNet. While evaluating their performance based on accuracy and processing speed. All the models are trained and tested on a custom dataset, comprising images of 10 distinct individuals. For yolov5 the images were annotated with bounding boxes for faces. The results show that yolov5 outperform against the traditional face recognition model in both accuracy and speed, achieved 99% accuracy for face recognition task with processing time as low as 2ms per step. The research provides important insights for improving face recognition technology and supports using YOLOv5 widely in real-world applications that need fast processing speed and accurate results.
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024
Keywords:
Facenet
,
face recognition
,
CNN
,
YOLOV5
,
RealTime Processing
References:
References are not available for this document.
Citations
Citations are not available for this document.