Standard

Age Recognition from Facial Images using Convolutional Neural Networks. / Pakulich, D. V.; Yakimov, S. A.; Alyamkin, S. A.

в: Optoelectronics, Instrumentation and Data Processing, Том 55, № 3, 01.05.2019, стр. 255-262.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Pakulich, DV, Yakimov, SA & Alyamkin, SA 2019, 'Age Recognition from Facial Images using Convolutional Neural Networks', Optoelectronics, Instrumentation and Data Processing, Том. 55, № 3, стр. 255-262. https://doi.org/10.3103/S8756699019030075

APA

Pakulich, D. V., Yakimov, S. A., & Alyamkin, S. A. (2019). Age Recognition from Facial Images using Convolutional Neural Networks. Optoelectronics, Instrumentation and Data Processing, 55(3), 255-262. https://doi.org/10.3103/S8756699019030075

Vancouver

Pakulich DV, Yakimov SA, Alyamkin SA. Age Recognition from Facial Images using Convolutional Neural Networks. Optoelectronics, Instrumentation and Data Processing. 2019 май 1;55(3):255-262. doi: 10.3103/S8756699019030075

Author

Pakulich, D. V. ; Yakimov, S. A. ; Alyamkin, S. A. / Age Recognition from Facial Images using Convolutional Neural Networks. в: Optoelectronics, Instrumentation and Data Processing. 2019 ; Том 55, № 3. стр. 255-262.

BibTeX

@article{f05fa01984104453b266eb780d2cfd2c,
title = "Age Recognition from Facial Images using Convolutional Neural Networks",
abstract = "A problem of age recognition from a human{\textquoteright}s face is developed with the popularization of convolutional neural networks. They make it possible to determine the specific features of faces, unseen by a human eye, and interpret them as age characteristics. Existing approaches to age recognition are analyzed. Data from existing sets for learning with subsequent correction for reducing the errors made in labels by acquisition algorithms are used. Neural networks are taught and tested using the resulting data. There is a problem with head rotation, whose solution is carried out using the images of faces rotated using the PRNet neural network.",
keywords = "age recognition, computer vision, convolutional neural network, deep neural networks",
author = "Pakulich, {D. V.} and Yakimov, {S. A.} and Alyamkin, {S. A.}",
year = "2019",
month = may,
day = "1",
doi = "10.3103/S8756699019030075",
language = "English",
volume = "55",
pages = "255--262",
journal = "Optoelectronics, Instrumentation and Data Processing",
issn = "8756-6990",
publisher = "Allerton Press Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Age Recognition from Facial Images using Convolutional Neural Networks

AU - Pakulich, D. V.

AU - Yakimov, S. A.

AU - Alyamkin, S. A.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - A problem of age recognition from a human’s face is developed with the popularization of convolutional neural networks. They make it possible to determine the specific features of faces, unseen by a human eye, and interpret them as age characteristics. Existing approaches to age recognition are analyzed. Data from existing sets for learning with subsequent correction for reducing the errors made in labels by acquisition algorithms are used. Neural networks are taught and tested using the resulting data. There is a problem with head rotation, whose solution is carried out using the images of faces rotated using the PRNet neural network.

AB - A problem of age recognition from a human’s face is developed with the popularization of convolutional neural networks. They make it possible to determine the specific features of faces, unseen by a human eye, and interpret them as age characteristics. Existing approaches to age recognition are analyzed. Data from existing sets for learning with subsequent correction for reducing the errors made in labels by acquisition algorithms are used. Neural networks are taught and tested using the resulting data. There is a problem with head rotation, whose solution is carried out using the images of faces rotated using the PRNet neural network.

KW - age recognition

KW - computer vision

KW - convolutional neural network

KW - deep neural networks

UR - http://www.scopus.com/inward/record.url?scp=85073644907&partnerID=8YFLogxK

U2 - 10.3103/S8756699019030075

DO - 10.3103/S8756699019030075

M3 - Article

AN - SCOPUS:85073644907

VL - 55

SP - 255

EP - 262

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 3

ER -

ID: 21939950