基于定向传质驱动的冷凝微液滴群三维指纹片段重构技术

张卫; 牛喻樱; 刘瑞; 赵玉刚

doi:10.7498/aps.74.20250413

摘要

指纹识别技术作为现代生活安全和信息保护的关键手段, 已广泛应用于日常生活的诸多领域. 传统2D指纹信息承载量不足, 难以满足高安全性需求. 近年提出的几种3D指纹技术虽各有优势, 但采样程序复杂且依赖于大型设备等问题限制了其实际应用. 本文提出一种基于冷凝微液滴群的简单快速三维指纹片段重构技术, 表明按压冷表面时指纹犁沟约束的水蒸气通过扩散凝结所形成的冷凝微液滴群蕴含着三维指纹信息, 并根据冷凝微液滴群的大小分布重建指纹片段. 通过与实测的指纹三维数据对比分析, 发现重构获取的数据与真实的指纹数据误差仅为9.3%, 具有良好的一致性. 该三维指纹重构方法可以便捷获取高信息承载量的指纹片段用于生物个体确认, 随着技术的不断优化和完善, 这一方法有助于在身份认证、安全防范及个人信息保护等领域发挥重要作用.

关键词:

Abstract

Fingerprint recognition technology plays a critical role in modern security and information protection. Traditional 2D fingerprint recognition methods are still limited due to an imbalance between growing security demands and inefficiency of encoding detailed information. Although various 3D fingerprint technologies have been introduced recently, their practical applications are restricted by complex sampling procedures and bulky equipment. This paper proposes a new 3D fingerprint fragments reconstruction method based on the condensation of microdroplet clusters, resulting in efficiently extracting detailed structural information from fingerprint patterns. By identifying the unique topological features of fingerprint valleys, a micrometer-scale vapor transport model is developed. A differential approach is used to divide the microdroplet clusters formed when a finger is pressed on a cold surface into discrete units. In each unit, the diffusion distance and mass transfer in the condensation process are calculated. Nonlinear regression techniques are then utilized to reconstruct the 3D fingerprint fragments. Furthermore, the experimental validation shows excellent consistency with premeasured fingerprint data, with a reconstruction error of less than 9.3%. It has made a significant improvement in capturing high-density fingerprint data in a short period of time, completing the data acquisition in less than 1 second. Compared with ultrasound imaging techniques, this method significantly shortens the acquisition time, which typically involve complex procedures. Additionally, it offers a more efficient alternative to deep learning methods, which require extensive data training and computational processes. This 3D fingerprint reconstruction method provides an efficient, low-cost and easy-to-operate solution. It holds the potential to significantly enhance personal identification and information protection systems, contributing to the advancement of 3D fingerprint recognition technology in practical applications.

Keywords:

作者及机构信息

1.
上海理工大学能源与动力工程学院, 上海市动力工程多相流与传热重点实验室, 上海　200093

2.
中国空气动力研究与发展中心, 结冰与防除冰重点实验室, 绵阳　621000

通信作者: 赵玉刚, ygzhao@usst.edu.cn

基金项目: 国家自然科学基金(批准号: 52276079)资助的课题.

Authors and contacts

1.
Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

2.
Key Laboratory of Icing and Anti/De-Icing, China Aerodynamics Research and Development Center, Mianyang 621000, China

Corresponding author: ZHAO Yugang, ygzhao@usst.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 52276079).

文章全文

参考文献

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施引文献

图 1 基于冷凝微液滴特征分析的3D指纹重构流程图

Fig. 1. 3D Fingerprint reconstruction process based on condensed droplet characteristics.

下载: 全尺寸图片幻灯片

图 2 冷凝微液滴图像采集　(a) 冷凝微液滴发生图像采集装置; (b) 冷凝微液滴带分布图; (c) 指纹结构显微图

Fig. 2. Condensate droplet image capture: (a) Image acquisition setup for condensed drop; (b) distribution of condensation droplet bands; (c) microscopic image of fingerprint structure.

下载: 全尺寸图片幻灯片

图 3 不同基底表面特性对液滴行为的影响比较　(a) 普通硅片与疏水硅片的接触角测量; (b) 普通硅片与疏水硅片上液滴冷凝效果对比

Fig. 3. Comparison of the effects of substrate surface properties on droplet behavior: (a) Contact angle measurements on standard and hydrophobic silicon wafers; (b) droplet condensation performance on standard and hydrophobic silicon wafers.

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图 4 毛细管模拟实验图　(a) 模具结构正视图; (b) 模具结构俯视图; (c) 模具按压冷表面所得冷凝微液滴分布图

Fig. 4. Capillary simulation experiment: (a) Front view of the mold structure; (b) top view of the mold structure; (c) distribution of condensate drops on the cold surface pressed by the mold.

下载: 全尺寸图片幻灯片

图 5 指纹犁沟模型建立及其有效性分析　(a) 犁沟内水蒸气输运微观示意图; (b) 冷凝微液滴传质面积计算示意图; (c) 指纹片段冷凝微液滴群图像; (d) 指纹片段水蒸气传质距离图; (e) 指纹片段重构结果3D图

Fig. 5. Establishment of the fingerprint valley model and effectiveness analysis: (a) Schematics of the microscopic vapor transfer within the valley; (b) schematic of mass transfer area calculation for condensed drops; (c) image of condensed droplet clusters on the fingerprint segment; (d) vapor mass transfer distance within the fingerprint segment; (e) 3D reconstruction of the fingerprint segment.

下载: 全尺寸图片幻灯片

图 6 三维指纹重构　(a) 指纹样品激光扫描图; (b) 扫描所得指纹片段三维形貌; (c) 指纹片段对应冷凝微液滴带分布图; (d) 重构所得指纹片段三维形貌

Fig. 6. 3D reconstruction of fingerprints: (a) Laser scan of the fingerprint sample; (b) 3D morphology of the fingerprint segment derived from the scan; (c) distribution of condensation droplet bands corresponding to the fingerprint segment; (d) 3D morphology of the reconstructed fingerprint segment.

下载: 全尺寸图片幻灯片

图 7 2D轮廓图　(a) 原始指纹片段轮廓图; (b) 重构指纹片段轮廓图

Fig. 7. 2D contour map: (a) Original fingerprint fragment contour map; (b) reconstructed fingerprint fragment contour map.

下载: 全尺寸图片幻灯片

图 8 三维指纹重构误差分析热图

Fig. 8. 3D Fingerprint reconstruction error analysis heatmap.

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图 9 不同温湿度组合下的冷凝微液滴分布与三维指纹重构形貌　(a) 冷凝微液滴分布图; (b) 三维指纹重构片段形貌图

Fig. 9. Condensed droplet distribution and 3D fingerprint reconstruction morphology under varying temperature and humidity conditions: (a) Distribution of condensed water droplets; (b) morphology of 3D fingerprint reconstruction fragment.

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map

[1]	Kolivand H, Asadianfam S, Akintoye K A, Rahim M S 2023 Multimed. Tools Appl. 82 33541 Google Scholar
[2]	Li H, Wei P, Hu P 2021 IEEE T. Multimedia 24 594 Google Scholar
[3]	Jan F, Min-Allah N, Agha S, Usman I, Khan I 2021 Multimed. Tools Appl. 80 4579 Google Scholar
[4]	Fei L K, Lu G M, Jia W, Teng S H, Zhang D 2018 IEEE T. Syst. Man Cy. A 49 346 Google Scholar
[5]	Lopes A T, De Aguiar E, De Souza A F, Oliveira-Santos T 2017 Pattern Recogn. 61 610 Google Scholar
[6]	Pinkus H 1963 JAMA 183 979 Google Scholar
[7]	Tian J Z, Zhang J Y, Li X Y, Zhou C C, Wu R L, Wang Y C, Huang S Y 2021 IEEE Access 9 160855 Google Scholar
[8]	Niu L H, Mantri N, Li C G, Xue C, Pang E 2011 TCM 6 18 Google Scholar
[9]	Zhang J Q, Shen G X, Saad W, Chowdhury K 2023 IEEE Commun. Mag. 61 110 Google Scholar
[10]	Zhou Z Y, Kumar A 2023 IEEE T. Inf. Forensics Secur. 19 441 Google Scholar
[11]	Cui Z, Feng J J, Li S H, Lu J W, Zhou J 2018 IEEE T. Inf. Forensics Secur. 13 3153 Google Scholar
[12]	An B W, Heo S, Ji S, Bien F, Park J U 2018 Nat. Commun. 9 2458 Google Scholar
[13]	Yi Y, Cao L C, Guo W, Luo Y P, Feng J J, He Q S, Jin G F 2013 Opt. Express 21 17108 Google Scholar
[14]	Liu F, Zhang D, Shen L L 2015 Neurocomputing 168 599 Google Scholar
[15]	Xie W, Song Z, Chung R 2013 Opt. Eng. 52 103103 Google Scholar
[16]	Chatterjee A, Bhatia V, Prakash S 2017 Opt. Lasers Eng. 95 1 Google Scholar
[17]	Wang Y C, Lau D L, Hassebrook L G 2010 Appl. Opt. 49 592 Google Scholar
[18]	Labati R D, Genovese A, Piuri V, Scotti F 2016 IEEE T. Syst. Man Cy. B 46 202 Google Scholar
[19]	Jiang X Y, Lu Y P, Tang H Y, Tsai J M, Ng E J, Daneman M J, Boser B E, Horsley D A 2017 Microsyst. Nanoeng. 3 17059 Google Scholar
[20]	Zhao C W, Li J, Lin M, Chen X, Liu Y 2022 IEEE T. Ultrason. Ferr. 69 2965 Google Scholar
[21]	Saijo Y, Kobayashi K, Okada N, Hozumi N, Hagiwara Y, Tanaka A, Iwamoto T 2008 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, Aug 20-25, 2008 p2173
[22]	李柯林, 黄丽淇, 杨洋 2021 电子测试 15 124 Google Scholar Li K L, Huang L Q, Yang Y 2021 Electronic Test. 15 124 Google Scholar
[23]	Liu Y H, Zhou B C, Han C Y, Guo T D, Qin J 2020 Appl. Intell. 50 397 Google Scholar
[24]	Shen G X, Zhang J Q, Marshall A, Peng L N, Wang X B 2021 IEEE J. Sel. Areas Commun. 39 2604 Google Scholar
[25]	王崇杰, 刘媛媛, 张博超, 张倩妮, 刘金艳, 金鸽, 张敏 2014 63 122801 Google Scholar Wang C J, Liu Y Y, Zhang B C, Zhang Q N, Liu J Y, Jin G, Zhang M 2014 Acta Phys. Sin. 63 122801 Google Scholar
[26]	吴春生, 李孝君, 吴浩 2022 刑事技术 47 88 Google Scholar Wu C S, Li X J, Wu H 2022 Forensic Sci. Tech. 47 88 Google Scholar
[27]	Zhu Y F, Yin X M, Hu J K 2023 IEEE T. Pattern Anal. 45 8358 Google Scholar
[28]	宋天一, 兰忠, 马学虎 2010 化工学报 61 839 Song T Y, Lan Z, Ma X H 2010 CIESC J. 61 839
[29]	宫保强, 姚宝国, 周子晗 2022 传感技术学报 35 14 Google Scholar Gong B Q, Yao B G, Zhou Z H 2022 Chin. J. Sensors Actuats. 35 14 Google Scholar
[30]	Guo W, Calija L M M, Xu P, Liu K, Senthil Kumar A 2021 Vacuum 190 110292 Google Scholar
[31]	Fletcher N H 1958 J Chem. Phys. 29 572 Google Scholar
[32]	曹治觉 2002 51 25 Google Scholar Cao Z J 2002 Acta Phys. Sin. 51 25 Google Scholar
[33]	Hong Z, Beysens D 1995 Langmuir 11 627 Google Scholar
[34]	Zhao Y G, Yang C 2016 Appl. Phys. Lett. 108 061065 Google Scholar
[35]	Arda O, Göksügür N, Tüzün Y 2014 Clin. Dermatol. 32 3 Google Scholar
[36]	Kückena M, Newell A C 2005 J. Theor. Biol. 235 71 Google Scholar
[37]	Wertheim K, Maceo A 2002 J. Forensic Identif. 52 35
[38]	陈云国, 方力 2007 刑事技术 6 49 Google Scholar Chen Y G, Fang L 2007 Forensic Sci. Technol. 6 49 Google Scholar
[39]	Yum S M, Baek I K, Hong D, Kim J, Jung K, Kim S, Eom K, Jang J, Kim S, Sattorov M, Lee M G, Kim S, Adams M J, Park G S 2020 Proc. Natl. A. Sci. 117 31665 Google Scholar
[40]	Sato T, Katayama C, Hayashida Y, Asanuma Y, Aoyama Y 2022 Exp. Dermatol. 31 1891 Google Scholar
[41]	Yu X J, Xiong Q Z, Luo Y M, Wang N S, Wang L L, Tey H L, Liu L B 2016 IEEE Photonic. Tech. Lett. 29 70 Google Scholar
[42]	Masi A D, Olla S, Presutti E 2019 J. Stat. Phys. 175 203 Google Scholar
[43]	Aum J, Kim J H, Jeong J 2015 IEEE Photonic. Tech. Lett. 28 163 Google Scholar
[44]	兰忠, 朱霞, 彭本利, 林勐, 马学虎 2012 61 150508 Google Scholar Lan Z, Zhu X, Peng B L, Lin M, Ma X H 2012 Acta Phys. Sin. 61 150508 Google Scholar
[45]	Skinner L M, Sambles J R 1972 J. Aerosol Sci. 3 199 Google Scholar

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基于定向传质驱动的冷凝微液滴群三维指纹片段重构技术