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

共 50 条

[31] DGGCNN: An Improved Generative Grasping Convolutional Neural Networks
Zhang, Zhenyu
Luo, Junqi
Liu, Jiyuan
Chen, Mingyou
Zhang, Shanjun
Zhu, Liucun
2022 ASIA CONFERENCE ON ADVANCED ROBOTICS, AUTOMATION, AND CONTROL ENGINEERING (ARACE 2022), 2022, : 57 - 61
[32] A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network
Ge, Huilin
Dai, Yuewei
Zhu, Zhiyu
Wang, Biao
APPLIED SCIENCES-BASEL, 2021, 11 (24):
[33] Masked Face Recognition Using Generative Adversarial Networks by Restoring the Face Closed Part
Chaoxiang Chen
I. Kurnosov
Guangdi Ma
Yang Weichen
S. Ablameyko
Optical Memory and Neural Networks, 2023, 32 : 1 - 13
[34] Masked Face Recognition Using Generative Adversarial Networks by Restoring the Face Closed Part
Chen, Chaoxiang
Kurnosov, I.
Ma, Guangdi
Weichen, Yang
Ablameyko, S.
OPTICAL MEMORY AND NEURAL NETWORKS, 2023, 32 (01) : 1 - 13
[35] Face recognition based on improved residual neural network
Chen Zhenzhou
Ding Pengcheng
PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 4626 - 4629
[36] A Study on Multi-pose Face Recognition Based on Improved Local Convolutional Neural Networks
Xu, Peijie
2022 6TH INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND INTELLIGENT CONTROL, ISCSIC, 2022, : 30 - 33
[37] A System for Disguised Face Recognition with Convolution Neural Networks
Hung, Kuo-Ming
Wu, Jin-An
Wen, Com Chia-Hung
Chen, Li-Ming
PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON DIGITAL MEDICINE AND IMAGE PROCESSING (DMIP 2018), 2018, : 65 - 69
[38] Face Recognition with Convolutional Neural Networks and Subspace Learning
Wan, Lihong
Liu, Na
Huo, Hong
Fang, Tao
2017 2ND INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC 2017), 2017, : 228 - 233
[39] Face recognition with facial mask application and neural networks
Grassi, Marco
Faundez-Zanuy, Marcos
COMPUTATIONAL AND AMBIENT INTELLIGENCE, 2007, 4507 : 709 - +
[40] Automatic face recognition system using neural networks
El-Bakry, HM
Abo-Elsoud, MA
Kamel, MS
ISCAS 2000: IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS - PROCEEDINGS, VOL III: EMERGING TECHNOLOGIES FOR THE 21ST CENTURY, 2000, : 543 - 546

← 1 2 3 4 5 →