Computer Vision-Based Age, Gender, Ethnicity Recognition
| dc.contributor.author | Doucene, Salah Eddine | |
| dc.contributor.author | Zehani, Mohamed Hached | |
| dc.contributor.author | Ghemougui, Abdessattar: Reporter | |
| dc.date.accessioned | 2024-06-30T13:46:36Z | |
| dc.date.available | 2024-06-30T13:46:36Z | |
| dc.date.issued | 2024-06-12 | |
| dc.description.abstract | Age, gender, and ethnicity recognition technology analyzes faces using computer vision, but accuracy can be impacted by lighting, pose, and image quality. This report investigates existing methods, trains deep models similar to VGG/ResNet architectures and a pre-trained model from SkillCate, then evaluates and discusses the results. It aims to improve recognition accuracy despite these challenges. | |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/42917 | |
| dc.language.iso | en | |
| dc.publisher | UNIVERSITY OF MOHAMED BOUDIAF – M’SILA, FACULTY OF MATHEMATICS AND COMPUTER SCIENCE, DEPARTMENT OF COMPUTER SCIENCE | |
| dc.subject | HUMANITIES and RELIGION::Languages and linguistics::Scandinavian languages::Norwegian language | |
| dc.subject | Gender and Ethnicity Recognition | |
| dc.subject | Computer Vision | |
| dc.title | Computer Vision-Based Age, Gender, Ethnicity Recognition | |
| dc.type | Thesis |