Classification and Prediction of Gender in Facial Images with CNN
- Witman Alvarado-Diaz(corresponding author),
- ,
- Avid Roman-Gonzalez
- Universidad de Ciencias y Humanidades
Publication Information
Output type
Original language
EnglishPages from-to (Number of pages)
Pages 53-62 (10 pages)Publication milestones
- Published - 2021
Publication status
Publisher
Springer Science and Business Media Deutschland GmbHPublication series
- Publication series name: Lecture Notes in Electrical Engineering
ISSN (Print): 1876-1100
ISSN (Electronic): 1876-1119
Volume: 762 LNEE
ISBN (Print)
9783030722074Publication IDs
- Scopus: 85107315672
Host publication title
Recent Advances in Electrical Engineering, Electronics and Energy - Proceedings of the CIT 2020Host 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%.
