Offline Facial Recognition Using Machine Learning Techniques For Examination Attendance & Logging.
- Author
- Taonga, Bhebhe E
- Title
- Offline Facial Recognition Using Machine Learning Techniques For Examination Attendance & Logging.
- Abstract
-
Abstract
This research project aimed to develop a machine learning-powered Android application for offline facial authentication of students during exams, attendance marking, and break logging. The mobile application is designed to capture and process facial features for subsequent recognition using a deep learning model. The proposed system comprises several processing stages, including image processing, feature extraction, and facial recognition. The facial recognition model integrated into the mobile application is based on a convolutional neural network (CNN) architecture trained on a large dataset of image samples.
- Date
- JUNE 2024
- Publisher
- BUSE
- Keywords
- machine learning-powered Android application, offline facial authentication, facial recognition model.
- Supervisor
- Mr. T. Mhlanganiso
- Item sets
- Department of Computer Science
- Media
-
Ernest Taonga Bhebe
Part of Offline Facial Recognition Using Machine Learning Techniques For Examination Attendance & Logging.