Fast OCT Based Internal and External Fingerprint Extraction Method

doi:10.3778/j.issn.1002-8331.2303-0370

Abstract

Abstract: To address the issue of slow internal and external fingerprint extraction algorithms in existing optical coherence tomography (OCT) techniques, especially those requiring time-consuming denoising operations on the raw volumetric data, a simple and fast OCT internal and external fingerprint extraction algorithm has been proposed. For 3D OCT fingerprint data, firstly, according to the maximum first-order differential value of each B-scan image, a curve covering the stratum corneum is fitted. Then, according to the sub-maximum of the first-order differential value ,a curve that basically crosses the ridge and valley of the contour at the junction of the active epidermis is fitted. Finally, at the positions of N pixels above and below the two curves, the gray value mean information near the two curves is used to spliced according to the sequence of B-scan images to generate internal and external fingerprint images. The optimal value of N is determined through experiments. Compared with existing internal and external fingerprint extraction algorithms, it is proved that the proposed algorithm can quickly extract internal and external fingerprint images without reducing fingerprint quality, and the extraction time of internal and external fingerprint is much faster than the hierarchical clustering algorithm and gradient-based methods.

Key words: internal fingerprint, external fingerprint, optical coherence tomography (OCT), fingerprint extraction

摘要： 针对现有光学层析成像（optical coherence tomography，OCT）技术中内外部指纹提取算法速度较慢的问题，尤其是许多算法在提取指纹之前需要耗时的原始体数据去噪操作，为了解决这一问题，提出了一种简单快速的OCT内外部指纹提取算法。对于OCT指纹体数据，首先根据每张轴向切片（B-scan）图像的一阶微分最大值，拟合一条基本覆盖角质层轮廓的曲线，然后再根据一阶微分的次最大值，拟合出一条基本穿过活性表皮交界处轮廓脊谷处的曲线，最后在这两个曲线上下各N个像素点的位置，利用两条曲线附近的灰度值均值信息，按B-scan图片顺序进行拼接，生成提取到的内外部指纹图像。通过实验确定了N的最佳取值，然后与现有的内外部指纹提取算法对比，证明所提出的算法能够在不降低指纹质量的情况下，快速地提取出内外部指纹图像，提取内外部指纹的时间比层次聚类算法和梯度法都快很多倍。

关键词: 内部指纹, 外部指纹, 光学层析相干技术, 指纹提取

ZHU Shengming, PU Shiliang, WANG Haixia, ZHANG Yilong, WANG Yingyu, WANG Kanlei. Fast OCT Based Internal and External Fingerprint Extraction Method[J]. Computer Engineering and Applications, 2024, 60(12): 245-251.

朱胜明, 浦世亮, 王海霞, 张怡龙, 王映宇, 王侃磊. 一种快速的OCT内外指纹提取方法[J]. 计算机工程与应用, 2024, 60(12): 245-251.

References

[1] SYED S A, VIVEK S B, IYYAKUTTI I G, et al. Robust biometric authentication system with a secure user template[J]. Image and Vision Computing, 2020, 104: 104004.
[2] 陈朋, 于洋, 马灵涛, 等. 自适应的OCT内外部指纹提取算法[J]. 计算机辅助设计与图形学学报, 2019, 31(6): 961-970.
CHEN P, YU Y, MA L T, et al. Adaptive OCT internal and external fingerprint extraction[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(6): 961-970.
[3] VIJAY A, VIVEK K. Pore detection in high-resolution fingerprint images using deep residual network[J]. Journal of Electronic Imaging, 2019, 31(6): 961-970.
[4] 王海霞, 王如欣, 张怡龙, 等. 基于轻量级3DUnet神经网络的OCT皮下汗腺提取研究[J]. 高技术通讯, 2022, 32(7): 737-748.
WANG H X, WANG R X, ZHANG Y L, et al. OCT subcutaneous sweat gland extraction based on a lightweight 3DUnet neural network[J]. High Technology Letters, 2022, 32(7): 737-748.
[5] RUGGERO D L, ANGELO G, ENRIQUE M, et al. A novel pore extraction method for heterogeneous fingerprint images using convolutional neural networks[J]. Pattern Recognition Letters, 2018, 113: 58-66.
[6] XU Y, LU G, LIU F, et al. Fingerprint pore extraction based on multi?scale morphology[C]//Proceedings 12th Chinese Conference on Biometric Recognition (CCBR 2017), Shenzhen, China, October 28-29, 2017. [S.l.]: Springer International Publishing, 2017: 288-295.
[7] ANKE B, ROLAND L, CHRISTOPH M. Internal fingerprint identification with optical coherence tomography[J]. IEEE Photonics Technology Letters, 2010, 22(7): 507-509.
[8] ZHONG Z W, ZHANG J L, LI Z F, et al. Depth-independent internal fingerprint based on optical coherence tomography[J]. Optics Express, 2021, 29: 16991-17000.
[9] LUKE N D, JAMES C, SHARAT S A. Internal fingerprint zone detection in optical coherence tomography fingertip scans[J]. Journal of Electronic Imaging, 2015, 24(2): 023027.
[10] DING B, WANG H, LIANG R, et al. End-to-end surface and internal fingerprint reconstruction from optical coherence tomography based on contour regression[J]. IEEE Transactions on Information Forensics and Security, 2022, 18: 162-176.
[11] DING B, WANG H, CHEN P, et al. Surface and internal fingerprint reconstruction from optical coherence tomography through convolutional neural network[J]. IEEE Transactions on Information Forensics and Security, 2020, 16: 685-700.
[12] MARCEL S, NIXON M S, LI S Z. Handbook of biometric anti-spoofing[M]. New York: Springer, 2019: 47-49.
[13] ZHANG W, LIU H, LIU F, et al. A uniform representation learning method for OCT-based fingerprint presentation attack detection and reconstruction[J]. arXiv:2209.12208, 2022.
[14] CHUGH T, JAIN A K. OCT fingerprints: resilience to presentation attacks[J]. arXiv:1908.00102, 2019.
[15] YU X, XIONG Q, LUO Y M. Contrast enhanced subsurface fingerprint detection using high-speed optical coherence tomography[J]. IEEE Photonics Technology Letters, 2016, 29(1): 70-73.
[16] PETER M. A note on optical coherence tomography (OCT)[J]. Journal of Clinical Chemistry and Laboratory Medicine, 2021(4): 196.
[17] SCHMITT J M. Optical coherence tomography (OCT): a review[J]. IEEE Journal of Selected Topics in Quantum Electronics, 1999, 5(4): 1205-1215.
[18] KHUTLANG R, NELWAMONDO F V. Novelty detection-based internal fingerprint segmentation in optical coherence tomography images[C]//Proceedings of the 2nd International Symposium on Computing and Networking. Los Alamitos: IEEE Computer Society Press, 2015: 556-559.
[19] LIU F, LIU G J, ZHAO Q J, et al. Robust and high-security fingerprint recognition system using optical coherence tomography[J]. Neurocomputing, 2020(102): 14-28.
[20] LIU F, ZENG W, ZHANG W, et al. Multi-layered minutiae extraction based on fusion-attention for OCT fingerprints[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2023, 5(2): 221-232.
[21] 牛瑞, 田爱玲, 王大森, 等. 数字全息测量系统的散斑噪声抑制[J]. 激光与光电子学进展, 2022, 59(16): 86-92.
NIU R, TIAN A L, WANG D S, et al. Speckle noise suppression for digital holographic measurement systems[J]. Laser & Optoelectronics Progress, 2022, 59(16): 86-92.
[22] 蔡鑫鑫, 张世宇, 陈强, 等. 结构保持生成对抗网络的SD-OCT图像去噪方法[J]. 计算机辅助设计与图形学学报, 2020, 32(5): 751-758.
CAI X X, ZHANG S Y, CHEN Q, et al. Structure preservation generative adversarial network for noise reduction in SD-OCT images[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(5): 751-758.
[23] 贺玉华, 杨明明. 基于生成对抗网络的OCT图像去噪方法[J]. 现代计算机, 2021(12): 87-91.
HE Y H, YANG M M. OCT image denoising method based on generative adversarial network[J]. Modern Computer, 2021(12): 87-91.
[24] SCHOTTENHAMML J, WüRFL T, PLONER S B, et al. Unsupervised OCT denoising using speckle split[J]. Investigative Ophthalmology & Visual Science, 2022, 63(7): 182-199.
[25] DING B J, WANG H X, CHEN P, et al. Subcutaneous sweat pore estimation from optical coherence tomography[J]. IET Image Processing, 2021, 15(13): 3267-3280.
[26] DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095.