Age Estimation System Using Deep Residual Network Classification Method

Arna Fariza, Mu'Arifin, Agus Zainal Arifin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

The human face has biometric properties that are important for providing age information because of the aging process of the face. Automatic Age estimation is a difficult problem because the relationship between facial images and age is not very linear. Deep residual network (Resnet) is a neural network convolution architecture that was easier to optimize and can gain accuracy results from a considerably increasing depth. In this paper, we propose a new approach age estimation on convolution neural network (CNN) using the deep residual network (Resnet) model. Through the literature, Resnet achieves superior results when compared with other state-of-the-art image classifications. We compare a new generation of deep residual network called ResNeXt with Resnet and a basic linier regression model architecture.We user UTKFace dataset to evaluate the performance of residual network for age estimation of the range 1-100 years old. The result shows that the ResNeXt-50 (32×4d) architecture achieves a better age estimation results than Resnet-50 and linier regression.

Original languageEnglish
Title of host publicationIES 2019 - International Electronics Symposium
Subtitle of host publicationThe Role of Techno-Intelligence in Creating an Open Energy System Towards Energy Democracy, Proceedings
EditorsAhmad Zainudin, Andhik Ampuh Yunanto
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages607-611
Number of pages5
ISBN (Electronic)9781728144498
DOIs
Publication statusPublished - Sept 2019
Event21st International Electronics Symposium, IES 2019 - Surabaya, Indonesia
Duration: 27 Sept 201928 Sept 2019

Publication series

NameIES 2019 - International Electronics Symposium: The Role of Techno-Intelligence in Creating an Open Energy System Towards Energy Democracy, Proceedings

Conference

Conference21st International Electronics Symposium, IES 2019
Country/TerritoryIndonesia
CitySurabaya
Period27/09/1928/09/19

Keywords

  • ResNeXt
  • age estimation
  • deep residual network
  • human face

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