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Classification and Prediction of Gender in Facial Images with CNN

*Corresponding author for this work
  • Universidad de Ciencias y Humanidades
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Peer-review

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 53-62 (10 pages)

Publication milestones

  • Published - 2021

Publication status

Published - 2021

Publisher

Springer Science and Business Media Deutschland GmbH

Publication series

  • Publication series name: Lecture Notes in Electrical Engineering
    ISSN (Print): 1876-1100
    ISSN (Electronic): 1876-1119
    Volume: 762 LNEE
9783030722074

Publication IDs

  • Scopus: 85107315672

Host publication title

Recent Advances in Electrical Engineering, Electronics and Energy - Proceedings of the CIT 2020

Host publication editors

  • Miguel Botto Tobar
  • Henry Cruz
  • Angela Díaz Cadena

Abstract

Computer technology development, the popularization of artificial intelligence, and facial recognition have become necessary for multiple applications. Both in the military and economic aspects, as it is gradually introduced into people’s lives, for example, in the use of facial recognition to unlock mobile phones. Since the 1990s, gender identification has begun to be studied through a photo of the face; it is worth mentioning that facial gender recognition is challenging in computer vision. This article is made to be applicable in marketing; in this way, it could offer differentiated products according to the clients’ gender. For this purpose, it has used public databases to classify the images of faces in men and women, with the implementation of a Convolutional Neural Network (CNN) model, which it obtained an efficiency in the classification of approximately 97%. It also carried out prediction tests in which the silver model achieved a hit rate of 86.25%.

Publication metrics

Metrics

Scopus
Citations

Related Event

Title

15th Multidisciplinary International Congress on Science and Technology, CIT 2020

Event type

Conference

Date

26/10/2020 - 30/10/2020

Location

QuitoEcuador