Access Control System of " Cinq laboratoires' U sing Face Recognition
| dc.contributor.author | Sabrina, Bouzobra | |
| dc.contributor.author | Soufiane, Boulehouache | |
| dc.contributor.author | Adel, Lahsasna | |
| dc.date.accessioned | 2025-04-22T08:18:44Z | |
| dc.date.available | 2025-04-22T08:18:44Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Security in laboratories represents a big challenge and major concern for authorities and governments worldwide, and the use of traditional systems to address this issue has not solved the problem. Face recognition technology is the latest and one of the most effective authentication AI-based technologies to be adopted for keyless access control systems. In our current research work, an access control system was developed using deep learning to restrict access to the laboratory to authorized members only. The data set was collected from 09 subjects who are working in the department of computer science, university of Skikda. In order to avoid the bias in our testing results, the data set was resampled using five cross-validation method (5-cv). The results achieved by our system were presented along with discussions and analysis of the key findings and features of the system. The proposed system achieved an accuracy rate as high as 97%. Finally, some challenging cases like pose variations and face with occlusion were presented to show how our system could handle these cases efficiently. Where the results proved good efficienc | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/4647 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Sciences | |
| dc.title | Access Control System of " Cinq laboratoires' U sing Face Recognition | |
| dc.title.alternative | Computer Systems | |
| dc.type | Masters degree thesis |