Gender and Ethnicity Recognition System Based on Convolutional Neural Networks

Title

Gender and Ethnicity Recognition System Based on Convolutional Neural Networks

Creator

Harishwaran, S.; Sambandam, Rakoth Kandan; Gokulapriya, R.

Description

The classification of Gender and Ethnicity has been utilized in diverse scenarios, specifically in the realm of human-computer interaction, visual surveillance, and electronic customer services. Predicting the gender and ethnicity of individuals presents a significant obstacle due to its complex characteristics. The escalating prevalence of social media has emphasized the utmost importance of independently predicting gender and race. In this research endeavor, a framework is utilized which utilizes a Convolutional Neural Network to forecast gender and ethnicity by utilizing various outputs starting from the initial stage. The models performance was evaluated using different metrics, including the F1-score, accuracy, precision, recall, and accuracy. The methodology is evaluated using the UTKFace dataset for predicting gender and ethnicity, and compared the model with previous study to understand which model is giving better accuracy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Source

Communications in Computer and Information Science;Volume;2126;pp.66-76

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Convolutional Neural Network; Ethnicity; Gender

Coverage

Harishwaran S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Gokulapriya R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 18650929; ISBN: 978-303180841-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-80842-5_6

https://www.scopus.com/pages/publications/85218444670?origin=resultslist