TELKOMNIKA Telecommunication, Computing, Electronics and Control
Semi-supervised auto-encoder for facial attributes recognition

Dublin Core

Title

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Semi-supervised auto-encoder for facial attributes recognition

Subject

Age estimation, Deep learning, Gender recognition, Softmax classifier, Supervised autoencoder

Description

The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new architecture of DSAE by adding the supervision to the classic model and we control the overfitting problem by regularizing the model. The pass from DSAE to the semi-supervised autoencoder (DSSAE) facilitates the supervision process and achieves an excellent performance to extract features. In this work we focused to estimate AGE jointly. The experiment results show that DSSAE is created to recognize facial features with high precision. The whole system achieves good performance and important rates in AGE using the MORPH II database

Creator

Soumaya Zaghbani, Nouredine Boujneh, Med Salim Bouhlel

Source

DOI: 10.12928/TELKOMNIKA.v18i4.14836

Publisher

Universitas Ahmad Dahlan

Date

August 2020

Contributor

Sri Wahyuni

Rights

ISSN: 1693-6930

Relation

http://journal.uad.ac.id/index.php/TELKOMNIKA

Format

PDF

Language

English

Type

Text

Coverage

TELKOMNIKA Telecommunication, Computing, Electronics and Control

Files

Collection

Tags

,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon , ,Repository, Repository Horizon University Indonesia, Repository Universitas Horizon Indonesia, Horizon.ac.id, Horizon University Indonesia, Universitas Horizon Indonesia, HorizonU, Repo Horizon ,

Citation

Soumaya Zaghbani, Nouredine Boujneh, Med Salim Bouhlel, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Semi-supervised auto-encoder for facial attributes recognition,” Repository Horizon University Indonesia, accessed November 14, 2024, https://repository.horizon.ac.id/items/show/3990.