Evaluating Contactless Fingerprint Segmentation for Interoperable Biometric Identification Systems

Euclides Napoleão Arcoverde Neto; Thiago Lopes Trugillo da Silveira

doi:10.22456/2175-2745.147966

Authors

Euclides Napoleão Arcoverde Neto Serviço Federal de Processamento de Dados (SERPRO) https://orcid.org/0009-0009-9498-4787
Thiago Lopes Trugillo da Silveira Universidade Federal de Santa Maria (UFSM) https://orcid.org/0000-0001-6788-2667

DOI:

https://doi.org/10.22456/2175-2745.147966

Keywords:

Object Detection, Image Segmentation, Fingerprint Recognition

Abstract

Fingerprint recognition is a popular and cost-effective biometric technology. Most existing systems require specialized hardware to capture fingerprints, but contactless fingerprint capture can be performed using smartphone images. This method reduces costs and enhances hygiene and usability. A new image processing workflow is needed to facilitate contactless fingerprint capture, with segmentation a critical step. This study explores the feasibility of using smartphone images for this purpose. It evaluates four deep learning models - SSD MobileNetV2, SSD MobileNetV2 FPN Lite, Mask R-CNN, and U-Net - using the ISPFDv2 dataset. Results show that Mask R-CNN performed best in segmenting fingertip regions, while SSD MobileNetV2 had the highest recognition accuracy against traditional fingerprint databases. These findings demonstrate the potential of deep learning for effective contactless fingerprint recognition using smartphones.

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References

[1] DABOUEI, A. et al. Deep Contactless Fingerprint Unwarping. In: ICB. [S.l.: s.n.], 2019.

[2] CARNEY, L. A. et al. A Multi-Finger Touchless Fingerprinting System: Mobile Fingerphoto and Legacy Database Interoperability. In: ICBBE. [S.l.: s.n.], 2017. ISBN 9781450354844.

[3] GROSZ, S. A. et al. C2CL: Contact to Contactless Fingerprint Matching. IEEE Trans. on Information Forensics and Security, 2022. ISSN 15566021.

[4] KAUBA, C. et al. Towards using police officers’ business smartphones for contactless fingerprint acquisition and enabling fingerprint comparison against contact-based datasets. Sensors, 2021. ISSN 14248220.

[5] NEUROTECHNOLOGY. Neurotechnology VeriFinger SDK v13.0. 2023. ⟨https://www.neurotechnology.com/verifinger.html⟩. Access: Oct. 17, 2023.

[6] ENGELSMA, J. J.; CAO, K.; JAIN, A. K. Learning a Fixed-Length Fingerprint Representation. IEEE Trans. Pattern Anal. Mach. Intell., v. 43, n. 6, 2021.

[7] SANKARAN, A. et al. On smartphone camera based fingerphoto authentication. BTAS, 2015.

[8] MALHOTRA, A. et al. On Matching Finger-Selfies Using Deep Scattering Networks. IEEE Trans. on Biometrics, Behavior, and Identity Science, v. 2, 2020. ISSN 26376407.

[9] RONNEBERGER, O.; FISCHER, P.; BROX, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention. [S.l.: s.n.], 2015. ISBN 978-3-319-24574-4.

[10] KUNSUK, S.; AREEKUL, V. Finger Photo Rescaling for Interoperability of Touchless and Touch-based Fingerprint Verification. In: SITIS. [S.l.: s.n.], 2023.

[11] UHL, A. et al. Improving Sensor Interoperability between Contactless and Contact-Based Fingerprints Using Pose Correction and Unwarping. IET Biometrics, 2023. ISSN 2047-4938.

[12] SANDLER, M. et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: CVPR. [S.l.: s.n.], 2018.

[13] BEHESHTI, N.; JOHNSSON, L. Squeeze U-Net: A Memory and Energy Efficient Image Segmentation Network. In: CVPR. [S.l.: s.n.], 2020.

[14] SILVA, J. L. et al. Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation. 2021.

[15] BRUNA, J.; MALLAT, S. Classification with scattering operators. In: CVPR. [S.l.: s.n.], 2011.

[16] BRUNA, J.; MALLAT, S. Invariant Scattering Convolution Networks. IEEE Trans. Pattern Anal. Mach. Intell., 2013.

[17] PRIESNITZ, J. et al. Deep Learning-Based Semantic Segmentation for Touchless Fingerprint Recognition. In: Pattern Recognition. [S.l.: s.n.], 2021. ISBN 978-3-030-68793-9.

[18] CHEN, L.-C. et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Computer Vision – ECCV. [S.l.: s.n.], 2018. ISBN 978-3-030-01233-5.

[19] LIU, W. et al. SSD: Single Shot MultiBox Detector. In: LEIBE, B. et al. (Ed.). Computer Vision – ECCV. [S.l.: s.n.], 2016. ISBN 978-3-319-46448-0.

[20] HE, K. et al. Mask R-CNN. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2020.

[21] CHEN, L. C. et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell., 2018. ISSN 01628828.

[22] BADRINARAYANAN, V.; KENDALL, A.; CIPOLLA, R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 2017.

[23] SUN, K. et al. High-Resolution Representations for Labeling Pixels and Regions. CoRR, 2019.

[24] KUMAR, S.; KUMAR, R.; SAAD. Real-Time Detection of Road-Based Objects using SSD MobileNet-v2 FPNlite with a new Benchmark Dataset. In: iCoMET. [S.l.: s.n.], 2023.

[25] TABASSI, E.; WILSON, C.; SCHLENOFF, C. Fingerprint Image Quality. [S.l.], 2004. Dispon´ıvel em: ⟨https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=905710⟩.

[26] TABASSI, E. et al. NIST Fingerprint Image Quality 2. [S.l.], 2021. Dispon´ıvel em: ⟨https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920087⟩.

[27] KIRILLOV, A. et al. Segment Anything. arXiv:2304.02643, 2023.

[28] HUANG, J. et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors. In: CVPR. [S.l.: s.n.], 2017. ISSN 1063-6919.

[29] HUBER, P. J. Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics, Institute of Mathematical Statistics, v. 35, n. 1, p. 73–101, 1964.

[30] REN, S. et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell., 2017. ISSN 01628828.

[31] LIN, T.-Y. et al. Focal Loss for Dense Object Detection. In: ICCV. [S.l.: s.n.], 2017.

[32] LIN, T. et al. Microsoft COCO: Common Objects in Context. CoRR, 2014.

[33] TENSORFLOW. TensorFlow 2 Detection Model Zoo. 2023. ⟨https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md⟩. Access: Sept. 25, 2023.

[34] ZAK, K. U-Net implementations in Keras. 2020. ⟨https://github.com/karolzak/keras-unet⟩. Access: Sept. 25, 2023.

[35] PRINCE, S. J. D. Understanding Deep Learning. MIT Press, 2023. Dispon´ıvel em: ⟨http://udlbook.com⟩.

[36] LIN, T.-Y. et al. Feature Pyramid Networks for Object Detection. In: CVPR. [S.l.: s.n.], 2017.

[37] WU, Y. et al. Detectron2. 2019. ⟨https://github.com/facebookresearch/detectron2⟩.

[38] ZUIDERVELD, K. J. Contrast Limited Adaptive Histogram Equalization. In: Graphics Gems IV. [S.l.: s.n.], 1994. ISBN 0123361559.

[39] QUOC, T. T. P.; LINH, T. T.; MINH, T. N. T. Comparing U-Net Convolutional Network with Mask R-CNN in Agricultural Area Segmentation on Satellite Images. In: NICS. [S.l.: s.n.], 2020.

[40] JUNG, S. et al. Benchmarking Deep Learning Models for Instance Segmentation. Applied Sciences, 2022. ISSN 2076-3417.

[41] CUI, Z.; FENG, J.; ZHOU, J. Monocular 3D Fingerprint Reconstruction and Unwarping. 2022. Dispon´ıvel em: ⟨https://arxiv.org/abs/2205.00967⟩.

[42] SÖLLINGER, D.; UHL, A. Optimizing contactless to contact-based fingerprint comparison using simple parametric warping models. In: 2021 IEEE International Joint Conference on Biometrics (IJCB). [S.l.: s.n.], 2021. p. 1–7.