TY - GEN
T1 - Classification and Prediction of Gender in Facial Images with CNN
AU - Alvarado-Diaz, Witman
AU - Meneses-Claudio, Brian
AU - Roman-Gonzalez, Avid
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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%.
AB - 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%.
KW - Artificial intelligence
KW - Convolutional Neural Network
KW - Deep learning
KW - Facial recognition
KW - Gender recognition
UR - http://www.scopus.com/inward/record.url?scp=85107315672&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72208-1_5
DO - 10.1007/978-3-030-72208-1_5
M3 - Contribución a la conferencia
AN - SCOPUS:85107315672
SN - 9783030722074
T3 - Lecture Notes in Electrical Engineering
SP - 53
EP - 62
BT - Recent Advances in Electrical Engineering, Electronics and Energy - Proceedings of the CIT 2020
A2 - Botto Tobar, Miguel
A2 - Cruz, Henry
A2 - Díaz Cadena, Angela
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th Multidisciplinary International Congress on Science and Technology, CIT 2020
Y2 - 26 October 2020 through 30 October 2020
ER -