[1]WANG Shanshan,GONG Changqing,QIN Huafeng,et al.Vein recognition algorithm combining K-nearest neighbor and graph iterative based on deep learning[J].CAAI Transactions on Intelligent Systems,2024,19(5):1149-1156.[doi:10.11992/tis.202307009]
Copy

Vein recognition algorithm combining K-nearest neighbor and graph iterative based on deep learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1149-1156 Column: 学术论文—机器感知与模式识别 Public date: 2024-09-05
Title:
Vein recognition algorithm combining K-nearest neighbor and graph iterative based on deep learning
Author(s):
WANG Shanshan1;  GONG Changqing1;  QIN Huafeng1;  WANG Jun2;  LI Yantao3;  YANG Shuqiang4
1. College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing 400067, China;
2. Information and Control Engineering Institute, China University of Mining and Technology, Xuzhou 221116, China;
3. School of Computing, Chongqing University, Chongqing 400044, China;
4. School of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
Keywords:
biometric recognition; palm vein recognition; image processing; deep learning; KNN algorithm; convolutional neural network; graph iterative algorithm; graph neural network
CLC:
TP391.4
DOI:
10.11992/tis.202307009
Abstract:
In recent years, deep learning has been widely applied in the extraction and recognition of vein features due to its excellent performance in computer vision. Usually, vein recognition models based on deep learning learn the mapping between a single input image and its label. This approach barely captures the connections between multiple vein images from different categories. To solve this problem, this study introduces a deep learning-based K-nearest neighbor iterative vein recognition algorithm. First, the algorithm extracts features from palm vein images by using advanced deep learning models. Then, it calculates the distances between an image to be classified and training images by using the K-nearest neighbor algorithm, which determines the K most similar images and their labels. A label propagation matrix and a label matrix are created from these feature vectors. Finally, a graph iteration algorithm is used to predict the classifications. Tests are conducted on palm vein datasets provided by Hong Kong Polytechnic University and Tongji University. Recognition accuracies of 99.67% and 92.72% are obtained for the two datasets, respectively.
References:
[1] 谭振林, 刘子良, 黄蔼权, 等. 掌静脉识别的深度学习方法综述[J]. 计算机工程与应用, 2024, 60(6): 55-67.
TAN Zhenlin, LIU Ziliang, HUANG Aiquan, et al. Review of deep learning methods for palm vein recognition[J]. Computer engineering and applications, 2024, 60(6): 55-67.
[2] 赵程, 马彦波, 景荣, 等. 基于面部识别和掌静脉识别的AFC支付技术研究[J]. 智能城市, 2023, 9(5): 13-17.
ZHAO Cheng, MA Yanbo, JING Rong, et al. Research on AFC payment technology based on facial recognition and palmar vein recognition[J]. Intelligent city, 2023, 9(5): 13-17.
[3] WU Zhendong, TIAN Longwei, LI Ping, et al. Generating stable biometric keys for flexible cloud computing authentication using finger vein[J]. Information sciences, 2018, 433: 431-447.
[4] KONO M. A new method for the identification of individuals by using of vein pattern matching of a finger[C]//Proc Fifth Symposium on Pattern Measurement. Yamaguchi: CiNii, 2000: 9-12.
[5] 刘伟业, 鲁慧民, 李玉鹏, 等. 指静脉识别技术研究综述[J]. 计算机科学, 2022, 49(S1): 1-11.
LIU Weiye, LU Huimin, LI Yupeng, et al. Survey on finger vein recognition research[J]. Computer science, 2022, 49(S1): 1-11.
[6] QIN Huafeng, LAN Qin, YU Chengbo. Region growth–based feature extraction method for finger-vein recognition[J]. Optical engineering, 2011, 50(5): 057208.
[7] SONG W, KIM T, KIM H C, et al. A finger-vein verification system using mean curvature[J]. Pattern recognition letters, 2011, 32(11): 1541-1547.
[8] MIURA N, NAGASAKA A, MIYATAKE T. Extraction of finger-vein patterns using maximum curvature points in image profiles[J]. IEICE - transactions on information and systems, 2007, E90-D(8): 1185-1194.
[9] QIU Xinwei, KANG Wenxiong, TIAN Senping, et al. Finger vein presentation attack detection using total variation decomposition[J]. IEEE transactions on information forensics and security, 2018, 13(2): 465-477.
[10] 杨如民, 许琳英, 余成波. 基于Hessian矩阵和Gabor滤波的手指静脉特征提取[J]. 兵器装备工程学报, 2019, 40(3): 103-107, 111.
YANG Rumin, XU Linying, YU Chengbo. Finger vein feature extraction based on Hessian matrix and Gabor filtering[J]. Journal of ordnance equipment engineering, 2019, 40(3): 103-107, 111.
[11] LEE E C, LEE H C, PARK K R. Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction[J]. International journal of imaging systems and technology, 2009, 19(3): 179-186.
[12] GU Gaojie, BAI Peirui, LI Hui, et al. Dorsal hand vein recognition based on transfer learning with fusion of LBP feature[C]//Biometric Recognition. Cham: Springer, 2021: 221-230.
[13] QIN Huafeng, EL YACOUBI M A, LIN Jihai, et al. An iterative deep neural network for hand-vein verification[J]. IEEE access, 2019, 7: 34823-34837.
[14] 胡娜, 马慧, 湛涛. 融合LBP纹理特征与B2DPCA技术的手指静脉识别方法[J]. 智能系统学报, 2019, 14(3): 533-540.
HU Na, MA Hui, ZHAN Tao. Finger vein recognition method combining LBP texture feature and B2DPCA technology[J]. CAAI transactions on intelligent systems, 2019, 14(3): 533-540.
[15] HOU Borui, YAN Ruqiang. ArcVein-arccosine center loss for finger vein verification[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 5007411.
[16] YANG Wenming, HUI Changqing, CHEN Zhiquan, et al. FV-GAN: finger vein representation using generative adversarial networks[J]. IEEE transactions on information forensics and security, 2019, 14(9): 2512-2524.
[17] WANG Wenhai, XIE Enze, LI Xiang, et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 548-558.
[18] KHANDELWAL U, LEVY O, JURAFSKY D, et al. Generalization through memorization: nearest neighbor language models[EB/OL]. (2019-11-01)[2023-07-12]. https://arxiv.org/abs/1911.00172.
[19] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[20] QIN Huafeng, EL-YACOUBI M A, LI Yantao, et al. Multi-scale and multi-direction GAN for CNN-based single palm-vein identification[J]. IEEE transactions on information forensics and security, 2021, 16: 2652-2666.
[21] YANG Weili, LUO Wei, KANG Wenxiong, et al. FVRAS-net: an embedded finger-vein recognition and AntiSpoofing system using a unified CNN[J]. IEEE transactions on instrumentation and measurement, 2020, 69(11): 8690-8701.
[22] 袁臣虎, 刘铁根, 李秀艳. 基于kNN-SVM的手背静脉虹膜和指纹融合身份识别[J]. 光电工程, 2013, 40(4): 101-105.
YUAN Chenhu, LIU Tiegen, LI Xiuyan. Identification algorithm for fusion of hand vein iris and fingerprint based on kNN-SVM[J]. Opto-electronic engineering, 2013, 40(4): 101-105.
[23] 刘辉玲, 陶洁, 邱磊. 基于Python的One-hot编码的实现[J]. 武汉船舶职业技术学院学报, 2021, 20(3): 136-139.
LIU Huiling, TAO Jie, QIU Lei. Implementation of one-hot encoding based on python[J]. Journal of Wuhan Institute of Shipbuilding Technology, 2021, 20(3): 136-139.
[24] ZHANG Lin, CHENG Zaixi, SHEN Ying, et al. Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset[J]. Symmetry, 2018, 10(4): 78.
[25] SHEN Jiaquan, LIU Ningzhong, XU Chenglu, et al. Finger vein recognition algorithm based on lightweight deep convolutional neural network[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 1-13.
[26] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132–7141.
Similar References:

Memo

-

Last Update: 2024-09-05

Copyright © CAAI Transactions on Intelligent Systems