基于球面Haar小波和卷积神经网络的飞行员虹膜识别

贾博; 冯孝鑫; 李军; 俞碧婷; 赵倩; 吴奇

doi:10.11999/JEIT190928

基于球面Haar小波和卷积神经网络的飞行员虹膜识别

doi: 10.11999/JEIT190928

贾博¹,
冯孝鑫²,
李军¹,
俞碧婷³,
赵倩¹,
吴奇^2, ,

1.
东航技术应用研发中心有限公司上海 201707
2.
上海交通大学电子信息与电气工程学院上海 200240
3.
伍伦贡大学澳大利亚伍伦贡 2500

基金项目: 国家自然科学基金(U1933125)

详细信息

作者简介:
贾博：男，1987年生，工程师，研究方向为航空大数据、民航安全

冯孝鑫：男，1997年生，硕士生，研究方向为信号处理、计算机视觉

李军：男，1968年生，一级飞行员，研究方向为民航运行、飞行理论

俞碧婷：女，1990年生，博士，研究方向为机器视觉、深度学习

赵倩：女，1994年生，飞行理论教员，研究方向为民航飞行教学

吴奇：男，1978年生，副教授，研究方向为视脑交互

通讯作者:
吴奇　wuqi7812@sjtu.edu.cn

中图分类号: TP181
计量
- 文章访问数: 1599
- HTML全文浏览量: 936
- PDF下载量: 87
- 被引次数: 0
出版历程
- 收稿日期: 2019-11-20
- 修回日期: 2021-01-15
- 网络出版日期: 2021-01-22
- 刊出日期: 2021-04-20

Pilot Iris Recognition Based on Spherical Haar Wavelet and Convolutional Neural Network

Bo JIA¹,
Xiaoxin FENG²,
Jun LI¹,
Biting YU³,
Qian ZHAO¹,
Qi WU^{2
, ,}

1.
China Eastern Technology Application R&D Center Co.Ltd., Shanghai 201707, China
2.
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
3.
University of Wollongong, Wollongong 2500, Australia

Funds: The National Natural Science Foundation of China (U1933125)

摘要

摘要: 虹膜识别面临两个重要的问题：一是如何精细分解与重构虹膜球面图像；二是如何识别虹膜图特征。虹膜表面几何位置信息是一种重要的信号，传统的虹膜识别通常使用虹膜图像的平面特征，然而人的眼睛是一种球体，从平面图像难以提取到虹膜球体的几何特征。针对平面特征容易出现虹膜纹理的扭曲和失真等问题，该文建议一种正交对称的球面Haar小波(OSSHW)基，对球面虹膜信号进行多尺度分解与重构，获得更精细的虹膜曲面几何特征，同时对比球谐函数和半正交或正交球面Haar小波基的虹膜球面信号特征提取能力。在此基础上，该文提出一种基于卷积神经网络(CNN)和正交对称的球面Haar小波的虹膜识别方法，它能够有效捕获虹膜球体曲面的局部精细特征，比半正交或正交球面Haar小波基具有更强的虹膜识别能力。
- 虹膜识别 /
- 球面Haar小波基 /
- 球面信号 /
- 正交对称
Abstract: Iris recognition faces two important issues. they are how to decompose finely and reconstruct the spherical image of the iris, and how to identify the characteristics of the iris. Conventional iris recognition uses usually the planar features of these iris images. However, the human eye is a sphere. The geometric position information of the iris surface is an important signal, but it is difficult to extract the geometric features of the iris sphere from the planar image. Considering the issue that the plane features are prone to distortion and lack fidelity of iris texture, an Orthogonal and Symmetric Spherical Haar Wavelet (OSSHW) basis is proposed to decompose and reconstruct the spherical iris signal to obtain stronger geometric features of iris surface. The comparison of the feature extraction ability to spherical signal by the spherical harmonics and the typical semi-orthogonal or nearly orthogonal spherical Haar wavelet is also presented. And then, an iris recognition method based on Convolutional Neural Networks (CNN) + OSSHW is proposed, which can effectively capture the local fine features of iris spherical surface, and has stronger ability in iris recognition than semi-orthogonal or nearly orthogonal spherical Haar wavelet bases.
- Iris recognition /
- Spherical Haar wavelet basis /
- Spherical signals /
- Orthogonal and symmetric

HTML全文

图 1 仿真实验流程图

下载: 全尺寸图片幻灯片

图 2 球面谐波函数重构图像

下载: 全尺寸图片幻灯片

图 3 球面三角形划分方案

下载: 全尺寸图片幻灯片

图 4 虹膜信号重构误差

下载: 全尺寸图片幻灯片

图 5 检测得到虹膜内外边缘

下载: 全尺寸图片幻灯片

图 6 检测得到睫毛和上眼睑

下载: 全尺寸图片幻灯片

图 7 分离出的虹膜图像

下载: 全尺寸图片幻灯片

图 8 卷积神经网络结构图

下载: 全尺寸图片幻灯片

表 1 使用5种球面Haar小波基进行虹膜信号重构$ {l}_{2} $误差(保留1.56%的小波系数，level=5)

小波基	虹膜信号
小波基	Bio Haar	Nielson	Bonneau	Pseudo Haar	OSSHW
1	0.2671	0.2432	0.2448	0.2535	0.2282
2	0.1917	0.1744	0.1717	0.1813	0.1690
3	0.2967	0.2720	0.2716	0.2860	0.2676
4	0.2519	0.2345	0.2340	0.2446	0.2310
5	0.2636	0.2457	0.2483	0.2571	0.2432

下载: 导出CSV

表 2 使用不同球面信号分析方法的识别准确率

网络结构	准确率(CCR)(%)	耗时(s)
Dhage等人^[14]	97.23	4.88
Bharath等人^[15]	95.93	0.10
DeepIris^[16]	95.50	/
Chen等人^[17]	98.00	/
IrisConvShallower	98.10	/
IrisConvDeeper	98.79	/
球谐函数 + CNN	91.07	4.36
Bio Haar + CNN	95.53	3.98
Nielson + CNN	96.42	3.34
Bonneau + CNN	97.32	3.27
Pseudo Haar + CNN	97.32	2.95
OSSHW + CNN	98.21	3.92

下载: 导出CSV

参考文献(17)

MA Li, WANG Yunhong, and TAN Tieniu. Iris recognition based on multichannel Gabor filtering[C]. The 5th Asian Conference on Computer Vision, Melbourne, Australia, 2002: 279–283.

YAO Peng, LI Jun, YE Xueyi, et al. Iris recognition algorithm using modified log-Gabor filters[C]. The 18th International Conference on Pattern Recognition (ICPR’06), Hong Kong, China, 2006: 461–464.

NABTI M and BOURIDANE A. An effective and fast iris recognition system based on a combined multiscale feature extraction technique[J]. Pattern Recognition, 2008, 41(3): 868–879. doi: 10.1016/j.patcog.2007.06.030

PROENÇA H and SANTOS G. Fusing color and shape descriptors in the recognition of degraded iris images acquired at visible wavelengths[J]. Computer Vision and Image Understanding, 2012, 116(2): 167–178. doi: 10.1016/j.cviu.2011.10.008

SANTOS G and HOYLE E. A fusion approach to unconstrained iris recognition[J]. Pattern Recognition Letters, 2012, 33(8): 984–990. doi: 10.1016/j.patrec.2011.08.017

TAN Tieniu, ZHANG Xiaobo, SUN Zhenan, et al. Noisy iris image matching by using multiple cues[J]. Pattern Recognition Letters, 2012, 33(8): 970–977. doi: 10.1016/j.patrec.2011.08.009

HUO Guang, LIU Yuanning, ZHU Xiaodong, et al. Secondary iris recognition method based on local energy-orientation feature[J]. Journal of Electronic Imaging, 2015, 24(1): 013033. doi: 10.1117/1.JEI.24.1.013033

KUMAR A, POTNIS A, and SINGH A. Iris recognition and feature extraction in iris recognition system by employing 2D DCT[J]. IRJET International Research Journal of Engineering and Technology, 2016, 3(12): 503–510.

刘元宁, 刘帅, 朱晓冬, 等. 基于特征加权融合的虹膜识别算法[J]. 吉林大学学报: 工学版, 2019, 49(1): 221–229.

LIU Yuanning, LIU Shuai, ZHU Xiaodong, et al. Iris recognition algorithm based on feature weighted fusion[J]. Journal of Jilin University:Engineering and Technology Edition, 2019, 49(1): 221–229.

OUYANG Wanli, ZHAO Tianle, CHAM W K, et al. Fast full-search-equivalent pattern matching using asymmetric haar wavelet packets[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 28(4): 819–833. doi: 10.1109/tcsvt.2016.2629621

WU E Q, ZHOU Guirong, ZHU Limin, et al. Rotated sphere haar wavelet and deep contractive auto-encoder network with fuzzy Gaussian SVM for pilot’s pupil center detection[J]. IEEE Transactions on Cybernetics, 2019, 51(1): 332–345. doi: 10.1109/TCYB.2018.2886012

XU Guangzhu, ZHANG Zaifeng, and MA Yide. A novel method for iris feature extraction based on intersecting cortical model network[J]. Journal of Applied Mathematics and Computing, 2018, 26(1/2): 341–352.

ALOYSIUS N and GEETHA M. A review on deep convolutional neural networks[C]. 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2017: 588–592.

DHAGE S S, HEGDE S S, MANIKANTAN K, et al. DWT-based feature extraction and radon transform based contrast enhancement for improved iris recognition[J]. Procedia Computer Science, 2015, 45: 256–265. doi: 10.1016/j.procs.2015.03.135

BHARATH B V, VILAS A S, MANIKANTAN K, et al. Iris recognition using radon transform thresholding based feature extraction with Gradient-based Isolation as a pre-processing technique[C]. The 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior, India, 2014: 1–8.

MINAEE S, ABDOLRASHIDI A. Deepiris: Iris recognition using a deep learning approach[J]. arXiv preprint arXiv: 1907.09380, 2019.

CHEN Ying, WANG Wenyuan, ZENG Zhuang, et al. Adapted deep convnets technology for robust iris recognition[J]. Journal of Electronic Imaging, 2019, 28(3): 033008.

施引文献

资源附件(0)

访问统计