Left or Right Hand Classification from Fingerprint Images Using a Deep Neural Network

被引：7

作者：

Kim, Junseob ^{[1
]}

Rim, Beanbonyka ^{[1
]}

Sung, Nak-Jun ^{[1
]}

Hong, Min ^{[2
]}

机构：

[1] Soonchunhyang Univ, Dept Comp Sci, Asan 31538, South Korea

[2] Soonchunhyang Univ, Dept Comp Software Engn, Asan 31538, South Korea

来源：

CMC-COMPUTERS MATERIALS & CONTINUA | 2020年 / 63卷 / 01期

关键词：

Deep Learning; convolution neural network; fingerprint classification;

D O I：

10.32604/cmc.2020.09044

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individual's lifetime and is a highly reliable and secure way to identify a certain individuals. AFIS (Automated Fingerprint Identification System) is a system used by Korean police for identifying a specific person by fingerprint. The AFIS system, however, only selects a list of possible candidates through fingerprints, the exact individual must be found by fingerprint experts. In this paper, we designed a deep learning system using deep convolution network to categorize fingerprints as coming from either the left or right hand. In this paper, we applied the Classic CNN (Convolutional Neural Network), AlexNet, Resnet50 (Residual Network), VGG-16, and YOLO (You Only Look Once) networks to this problem, these are deep learning architectures that have been widely used in image analysis research. We used total 9,080 fingerprint images for training and 1,000 fingerprint to test the performance of the proposed model. As a result of our tests, we found the ResNet50 network performed the best at determining if an input fingerprint image came from the left or right hand with an accuracy of 96.80%.

引用

页码：17 / 30

页数：14

共 20 条

[11] Binary Hashing CNN Features for Action Recognition [J].

Li, Weisheng ;

Feng, Chen ;

Xiao, Bin ;

Chen, Yanquan .

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2018, 12 (09) :4412-4428

[12] Deep Representations for Iris, Face, and Fingerprint Spoofing Detection [J].

Menotti, David ;

Chiachia, Giovani ;

Pinto, Allan ;

Schwartz, William Robson ;

Pedrini, Helio ;

Falcao, Alexandre Xavier ;

Rocha, Anderson .

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2015, 10 (04) :864-879

[13] Fast Fingerprint Classification with Deep Neural Networks [J].

Michelsanti, Daniel ;

Ene, Andreea-Daniela ;

Guichi, Yanis ;

Stef, Rares ;

Nasrollahi, Kamal ;

Moeslund, Thomas B. .

PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 5, 2017, :202-209

[14] Fingerprint Liveness Detection Using Convolutional Neural Networks [J].

Nogueira, Rodrigo Frassetto ;

Lotufo, Roberto de Alencar ;

Machado, Rubens Campos .

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2016, 11 (06) :1206-1213

[15]

Redmon J., 2018, ARXIV E PRINTS

[16] Malicious Software Classification using Transfer Learning of ResNet-50 Deep Neural Network [J].

Rezende, Edmar ;

Ruppert, Guilherme ;

Carvalho, Tiago ;

Ramos, Fabio ;

de Geus, Paulo .

2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, :1011-1014

[17] Vehicle Detection in Aerial Images Based on Hyper Feature Map in Deep Convolutional Network [J].

Shen, Jiaquan ;

Liu, Ningzhong ;

Sun, Han ;

Tao, Xiaoli ;

Li, Qiangyi .

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019, 13 (04) :1989-2011

[18]

Shrein J.M., 2017, P 2017 IEEE S SERIES, P1

[19]

Su HR, 2017, INT CONF ACOUST SPEE, P2057, DOI 10.1109/ICASSP.2017.7952518

[20] Convolutional Neural Network with Particle Filter Approach for Visual Tracking [J].

Tyan, Vladimir ;

Kim, Doohyun .

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2018, 12 (02) :693-709

← 1 2 →