Open AccessMethods for emotions, mood, gender and age recognition
Author(s) -
Denis Pribavkin,
Pavel Yakimov,
Molodogvardejskaya street Photonics Ras
Publication year - 2019
Publication title -
ceur workshop proceedings
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.177
H-Index - 52
ISSN - 1613-0073
DOI - 10.18287/1613-0073-2019-2416-542-548
Subject(s) - convolutional neural network , mood , computer science , metadata , emotion recognition , field (mathematics) , deep learning , artificial intelligence , software , machine learning , psychology , world wide web , social psychology , mathematics , pure mathematics , programming language
Recognition on images not only of shapes, but also of metadata is becoming increasingly popular among researchers in the field of convolutional neural networks and deep learning. This article provides an analytical overview of modern software solutions that recognize the images of emotions, mood, gender and age of a person. Enthusiasts invent all new and new architectures of convolutional neural networks, allowing to solve the tasks with considerable recognition accuracy.