Implementation of A Smart Student Attendance System based on Facial Recognition
| dc.contributor.author | Chouiter, Djamal | |
| dc.contributor.author | Reporter: Heraguemi, KamelEddine | |
| dc.date.accessioned | 2022-07-21T12:05:28Z | |
| dc.date.available | 2022-07-21T12:05:28Z | |
| dc.date.issued | 2022-06-10 | |
| dc.description.abstract | Face recognition is very important technologies today, especially after extensive performance upgrades over the past decade, and these systems are now popular in areas such as security and commerce. In the traditional method, it is difficult to manage a large number of students in the classroom, since it takes time and has a high risk of error when entering data into the system, so it is not recommended. But the real difficulty lies in implementing an accurate, real-time attendance system. Real-time facial recognition is a convenient way to deal with the large number of students who attend every day. In this paper, a real-time presence detection system was created, and using the OpenCV library, the video is read as frames. This was developed this system based on the dlib model using deep learning techniques, which has an accuracy of 99.37, on the Labeled Faces in the Wild benchmark dataset, combined with the library simple Face Recognition library in python. For face detection, we used Histogram of Oriented Gradients (HOG). And for face recognition, we apply Euclidean distance calculation to identify the input face, our system is capable of recognizing multiple faces, and it will be a successful way to manage student attendance. | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/30887 | |
| dc.language.iso | en | en_US |
| dc.publisher | UNIVERSITY of M'SILA | en_US |
| dc.subject | Face Detection, Face Recognition, Deep Learning, OpenCv, Dlib, HOG, Face Recognition Library. | en_US |
| dc.title | Implementation of A Smart Student Attendance System based on Facial Recognition | en_US |
| dc.type | Thesis | en_US |