Cyclic Generative Neural Networks for Improved Face Recognition in Nonstandard Domains

A system of methods for improving the quality of face recognition from infrared images is described. For testing the recognition algorithm in a multidomain environment, a database of ordinary and infrared face images is collected. An algorithm based on cyclic generative neural networks is developed....

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Vydané v:Journal of computer & systems sciences international Ročník 57; číslo 4; s. 620 - 625
Hlavní autori: Grinchuk, O. V., Tsurkov, V. I.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Moscow Pleiades Publishing 01.07.2018
Springer Nature B.V
Predmet:
Algorithms
Control
Engineering
Face recognition
Facial recognition technology
Image quality
Infrared imagery
Mechatronics
Neural networks
Object recognition
Pattern Recognition and Image Processing
Robotics
ISSN:1064-2307, 1555-6530
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Shrnutí:A system of methods for improving the quality of face recognition from infrared images is described. For testing the recognition algorithm in a multidomain environment, a database of ordinary and infrared face images is collected. An algorithm based on cyclic generative neural networks is developed. This algorithm makes it possible to transform images from the color domain into the infrared domain, which significantly increases the size of the training sample. It is shown that fine-tuning the recognition algorithm using the generated infrared images improves the recognition result on the test sample.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1064-2307
1555-6530
DOI:10.1134/S1064230718040093