Cyclic Generative Neural Networks for Improved Face Recognition in Nonstandard Domains

被引：1

作者：

Grinchuk, O., V ^{[1
]}

Tsurkov, V., I ^{[2
]}

机构：

[1] Moscow Inst Phys & Technol, Dolgoprudnyi 141700, Moscow Oblast, Russia

[2] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Dorodnicyn Comp Ctr, Moscow 119333, Russia

来源：

JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL | 2018年 / 57卷 / 04期

基金：

俄罗斯基础研究基金会;

关键词：

STEREO IMAGES; IDENTIFICATION; ALGORITHMS; OPTIMIZATION; OBJECTS;

D O I：

10.1134/S1064230718040093

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：620 / 625

页数：6

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