| dc.creator | Coronel, Franklin | |
| dc.creator | Barreno, Norma | |
| dc.creator | Muñoz, Paúl | |
| dc.creator | Zabala-Blanco, David | |
| dc.creator | Onofa, Noemí | |
| dc.creator | Flores-Calero, Marco | |
| dc.date | 2023-06-05T20:29:58Z | |
| dc.date | 2023-06-05T20:29:58Z | |
| dc.date | 2022 | |
| dc.date.accessioned | 2024-05-02T20:31:20Z | |
| dc.date.available | 2024-05-02T20:31:20Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/4833 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9275068 | |
| dc.description | This paper presents a web application to control personnel access to a work area without contact; this makes it ideal to help combat the Covid-19 health emergency. For its implementation, deep learning and computer vision techniques have been used for face detection and recognition. The system consists of four phases, the first one aimed at detecting and aligning the face with deep learning algorithms. The second phase obtains the facial features to recognize different people. The third phase consists of implementing a module that detects face impersonation, and significantly prevents possible attacks on the system by identifying whether the face is real or fake; and the last phase is the design and development of the web interface. This interface performs the communication of the algorithms, the users and the administration. In order to evaluate this proposal, several experiments have been carried out under diverse real conditions. The main results to correctly identify the user show that it has an accuracy of 99 %, in an estimated time of 3 seconds, in the range of 20 cm to 90 cm away, with respect to the camera. In addition, the system is capable of identifying users wearing masks or glasses, in this case with an accuracy of 95% in 4 seconds. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | 2022 IEEE Colombian Conference on Communications and Computing (COLCOM), 1-6 | |
| dc.subject | Deep learning | |
| dc.subject | COVID-19 | |
| dc.subject | Access control | |
| dc.subject | Face recognition | |
| dc.subject | Glass | |
| dc.subject | Cameras | |
| dc.subject | Proposals | |
| dc.subject | Personnel | |
| dc.subject | Facial features | |
| dc.title | Web-based personal access control system using facial recognition with deep learning techniques | |
| dc.type | Article | |
