This is a study on facial information analysis technology for estimating gender and age, and poses are estimated using a transformation relationship matrix between the camera coordinate system and the world coordinate system for estimating the pose of a face image. Gender classification was relatively simple compared to age estimation, and age estimation was made possible using deep learning-based facial recognition technology. A comparative CNN was proposed to calculate the experimental results using the purchased database and the public database, and deep learning-based gender classification and age estimation performed at a significant level and was more robust to environmental changes compared to the existing machine learning techniques.
@article{arxiv.2111.09303,
title = {Facial Information Analysis Technology for Gender and Age Estimation},
author = {Gilheum Park and Sua Jung},
journal= {arXiv preprint arXiv:2111.09303},
year = {2021}
}