Computer Vision-Based Age, Gender, Ethnicity Recognition
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Date
2024-06-12
Journal Title
Journal ISSN
Volume Title
Publisher
UNIVERSITY OF MOHAMED BOUDIAF – M’SILA, FACULTY OF MATHEMATICS AND COMPUTER SCIENCE, DEPARTMENT OF COMPUTER SCIENCE
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.
Description
Keywords
HUMANITIES and RELIGION::Languages and linguistics::Scandinavian languages::Norwegian language, Gender and Ethnicity Recognition, Computer Vision