Dorsal hand vein recognition based on convolutional neural networks

Haipeng Wan; Lei Chen; Hong Song; Jian Yang

doi:10.1109/BIBM.2017.8217830

Dorsal hand vein recognition based on convolutional neural networks

Haipeng Wan, Lei Chen, Hong Song, Jian Yang

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

38 Citations (Scopus)

Abstract

In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian smoothing filter algorithm were used to preprocess the images. Then Reference-CaffeNet AlexNet and VGG depth CNN were trained to extract image feature. Finally, logistic regression was applied for identification. The experimental results on two different size of dataset shown that the depth of network and size of data set size have different degree effect on recognition rate, the dorsal hand vein recognition rate based on VGG-19 reaches 99.7%. In this paper, we also explored the feasibility of ensemble learning on SqueezeNet. The recognition rate declined slightly with 99.52%, but the model size has been decreased sharply.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Editors	Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1215-1221
Number of pages	7
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217830
Publication status	Published - 15 Dec 2017
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: 13 Nov 2017 → 16 Nov 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume	2017-January

Conference

Conference	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/Territory	United States
City	Kansas City
Period	13/11/17 → 16/11/17

Keywords

convolutional neural network
deep learning
dorsal hand vein recognition
ensemble learning

Access to Document

10.1109/BIBM.2017.8217830

Cite this

Wan, H., Chen, L., Song, H., & Yang, J. (2017). Dorsal hand vein recognition based on convolutional neural networks. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C.-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 1215-1221). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217830

Wan, Haipeng ; Chen, Lei ; Song, Hong et al. / Dorsal hand vein recognition based on convolutional neural networks. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1215-1221 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).

@inproceedings{fe24d3f6253d4e5b99357b67503af5fc,

title = "Dorsal hand vein recognition based on convolutional neural networks",

abstract = "In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian smoothing filter algorithm were used to preprocess the images. Then Reference-CaffeNet AlexNet and VGG depth CNN were trained to extract image feature. Finally, logistic regression was applied for identification. The experimental results on two different size of dataset shown that the depth of network and size of data set size have different degree effect on recognition rate, the dorsal hand vein recognition rate based on VGG-19 reaches 99.7%. In this paper, we also explored the feasibility of ensemble learning on SqueezeNet. The recognition rate declined slightly with 99.52%, but the model size has been decreased sharply.",

keywords = "convolutional neural network, deep learning, dorsal hand vein recognition, ensemble learning",

author = "Haipeng Wan and Lei Chen and Hong Song and Jian Yang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",

year = "2017",

month = dec,

day = "15",

doi = "10.1109/BIBM.2017.8217830",

language = "English",

series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1215--1221",

editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",

booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

address = "United States",

}

Wan, H, Chen, L, Song, H & Yang, J 2017, Dorsal hand vein recognition based on convolutional neural networks. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1215-1221, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 13/11/17. https://doi.org/10.1109/BIBM.2017.8217830

Dorsal hand vein recognition based on convolutional neural networks. / Wan, Haipeng; Chen, Lei; Song, Hong et al.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1215-1221 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dorsal hand vein recognition based on convolutional neural networks

AU - Wan, Haipeng

AU - Chen, Lei

AU - Song, Hong

AU - Yang, Jian

PY - 2017/12/15

Y1 - 2017/12/15

N2 - In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian smoothing filter algorithm were used to preprocess the images. Then Reference-CaffeNet AlexNet and VGG depth CNN were trained to extract image feature. Finally, logistic regression was applied for identification. The experimental results on two different size of dataset shown that the depth of network and size of data set size have different degree effect on recognition rate, the dorsal hand vein recognition rate based on VGG-19 reaches 99.7%. In this paper, we also explored the feasibility of ensemble learning on SqueezeNet. The recognition rate declined slightly with 99.52%, but the model size has been decreased sharply.

AB - In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian smoothing filter algorithm were used to preprocess the images. Then Reference-CaffeNet AlexNet and VGG depth CNN were trained to extract image feature. Finally, logistic regression was applied for identification. The experimental results on two different size of dataset shown that the depth of network and size of data set size have different degree effect on recognition rate, the dorsal hand vein recognition rate based on VGG-19 reaches 99.7%. In this paper, we also explored the feasibility of ensemble learning on SqueezeNet. The recognition rate declined slightly with 99.52%, but the model size has been decreased sharply.

KW - convolutional neural network

KW - deep learning

KW - dorsal hand vein recognition

KW - ensemble learning

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U2 - 10.1109/BIBM.2017.8217830

DO - 10.1109/BIBM.2017.8217830

M3 - Conference contribution

AN - SCOPUS:85046016873

T3 - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

SP - 1215

EP - 1221

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

A2 - Yoo, Illhoi

A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

Y2 - 13 November 2017 through 16 November 2017

ER -

Wan H, Chen L, Song H , Yang J. Dorsal hand vein recognition based on convolutional neural networks. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu CR, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1215-1221. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). doi: 10.1109/BIBM.2017.8217830

Dorsal hand vein recognition based on convolutional neural networks

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