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Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet.

Hieu Trung Huynh^1,2, Hoang Nguyen¹

¹Vietnamese-German University, Binh Duong new city, Binh Duong Vietnam.

SN Computer Science

|October 16, 2020

Summary

This summary is machine-generated.

This study introduces a Wide ResNet model for estimating age and gender in Asian faces, trained on a new Asian face dataset. The model shows promising performance, comparable to existing estimators on specific datasets.

Keywords:

Age and gender estimation Convolutional neural network Deep learning ResNet Wide ResNet

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Area of Science:

Computer Vision
Machine Learning
Biometrics

Background:

Facial analysis for age and gender estimation is crucial for demographics in retail and marketing.
Existing models often lack diversity, performing poorly on non-Caucasian faces due to biased training data.

Purpose of the Study:

To develop an accurate age and gender estimation model for Asian faces.
To address the limitations of current models trained on Western facial features.

Main Methods:

Developed a Wide ResNet model tailored for facial feature analysis.
Utilized a newly curated and improved Asian face database for model training.

Main Results:

The Wide ResNet model demonstrated promising results in estimating age and gender for Asian individuals.
Performance was comparable to Microsoft's how-old API estimator on a specific dataset.

Conclusions:

The developed model offers an applicable solution for demographic analysis of Asian populations.
Highlights the importance of diverse datasets in training robust facial estimation models.