Facial Information Analysis Technology for Gender and Age Estimation

TL;DR

This paper explores facial analysis technology for gender and age estimation, demonstrating that deep learning-based methods outperform traditional techniques in accuracy and robustness across different environments.

Contribution

It introduces a comparative CNN model for age and gender estimation, highlighting improvements over existing machine learning methods in accuracy and environmental robustness.

Findings

Deep learning-based gender classification is more accurate than traditional methods.

Age estimation using CNN achieves high accuracy with various databases.

Deep learning methods are more robust to environmental changes.

Abstract

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.