A Computer Vision Non-Contact 3D System to Improve Fingerprint Acquisition

Authors

  • Georgios Balogiannis Laboratory of Biomedical Optics and Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, Zografou Campus, Athens 15780, Greece.
  • Dido Yova Laboratory of Biomedical Optics and Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, Zografou Campus, Athens 15780, Greece.
  • Konstantinos Politopoulos Laboratory of Biomedical Optics and Applied Biophysics, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, Zografou Campus, Athens 15780, Greece.

Keywords:

Biometrics, fingerprint, 3D reconstruction, non-contact, single-view, fringe projection.

Abstract

The fingerprint is one of the most important biometrics, with many acquisition methods developed over the years. Traditional 2D acquisition techniques produce nonlinear distortions due to the forced flattening of the finger onto a 2D surface. These random elastic deformations often introduce matching errors, making 2D techniques less reliable. Inevitably non-contact 3D capturing techniques were developed in an effort to deal with these problems. In this study we present a novel non-contact single camera 3D fingerprint reconstruction system based on fringe projection and a new model for approximating the epidermal ridges. The 3D shape of the fingerprint is reconstructed from a single 2D shading image in two steps. First the original image is decomposed into structure and texture components by an advanced Meyer algorithm. The structural component is reconstructed by a classical fringe projection technique. The textural component, containing the fingerprint information, is restored using a specialized photometric algorithm we call Cylindrical Ridge Model (CRM). CRM is a photometric algorithm that takes advantage of the axial symmetry of the ridges in order to integrate the illumination equation. The two results are combined together to form the 3D fingerprint, which is then digitally unfolded onto a 2D plane for compatibility with traditional 2D impressions. This paper describes the prototype 3D imaging system developed along with the calibration procedure, the reconstruction algorithm and the unwrapping process of the resulting 3D fingerprint, necessary for the performance evaluation of the method. 

References

. J.A.Unar et al. “A review of biometric technology along with trends and prospects”. Pattern Recognition, vol. 47, pp. 2673–2688, 2014.

. F. Liu, D. Zhang. “3D fingerprint reconstruction system using feature correspondences and prior estimated finger model”. Pattern Recognition. vol. 47, pp. 178-193, 2013.

. S. Huang et al. “3D fingerprint imaging system based on full-field fringe projection profilometry”. Optics and Lasers in Engineering, vol. 52, pp 123-130, 2013.

. Qijun Zhao et al. “3D to 2D fingerprints: Unrolling and distortion correction”, in Proceedings of Biometrics (IJCB)-International Joint Conference on Biometrics Compendium, IEEE, 2011.

. R. Hartley. Multiple view geometry in computer vision. Cambridge, U.K: Cambridge University Press, 2000.

. C. Hernandez et al. “Multi-view photometric stereo”, in IEEE T. Pattern Anal, vol. 30, 2008, pp. 548–554.

. F. Blais et al. “Practical considerations for a design of a high precision 3D laser scanner system”. Proceedings of SPIE 959, 1988, pp. 225–246.

. B. Bradley et al. “A simple, low cost, 3D scanning system using the laser light-sectioning method”, in Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Victoria, Vancouver Island, Canada, May2002, pp. 299–304.

. G. Hu, G. Stockman. “3D surface solution using structured light and constraint propagation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 390–402, 1989.

. M. Kucken, A. C. Newell. “Fingerprint formation”. Journal of Theoretical Biology, vol. 235, pp. 71–83, 2005.

. Lee O et al. “An optimized in vivo multiple-baseline stereo imaging system for skin wrinkles”. Optics Communications, vol. 283, pp. 4840–4845, 2010.

. J. Huang, Q. Wu. “A new reconstruction method based on fringe projection of three-dimensional measuring system”. Optics and Lasers in Engineering, vol. 52, pp. 115-122, 2013.

. M. Oren, S. K. Nayar. “Generalization of the Lambertian Model and Implications for Machine Vision”. International Journal of Computer Vision, vol. 14, pp. 227-251, 1995.

. O. Faugeras. Three-dimensional Computer Vision: A Geometric Viewpoint. Cambridge Massachusetts, USA: MIT Press, 2001.

. B. K. P. Horn. Robot vision. Cambridge Massachusetts, USA: MIT Electrical Engineering and Computer Science, MIT Press, 1986.

. D. Gorpas, K. Politopoulos, D. Yova. “A binocular machine vision system for three-dimensional surface measurement of small objects”. Computerized Medical Imaging and Graphics, vol 31, pp. 625-637, 2007.

. Soweon Yoon. “Fingerprint recognition: models and applications”. PhD Thesis, Michigan State University, USA, 2014.

. J.-F. Aujol et al. “Image decomposition into a bounded variation component and an oscillating component”. Journal of Mathematical Imaging and Vision, vol. 22, pp. 71–88, 2005.

. J.-F. Aujol et al. “Image decomposition application to SAR images”. Lecture Notes in Computer Science, vol. 2695, pp. 297-312, 2003.

. Y. Meyer. The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures, Topic: “Oscillating patterns in image processing and in some nonlinear evolution equations”. American Mathematical Society Boston, MA, USA, 2001.

. A. Chambolle. “An algorithm for total variation minimization and applications”. Journal of Mathematical Imaging and Vision, vol. 20, pp. 89–97, 2004.

. J.-F. Aujol et al. “Structure-texture image decomposition - modeling, algorithms, and parameter selection”. International Journal of Computer Vision, vol. 67, pp. 11-136, 2005.

. L. Rudin et al. “Nonlinear total variation based noise removal algorithms”. Physica D, vol. 60, pp. 259-268, 1992.

. L. A. Vese, S. J. Osher. “Modeling textures with total variation minimization and oscillating patterns in image processing”. Journal of Scientific Computing, vol. 19, pp. 553-572, 2002.

. Y. Chen et al. “3D touchless fingerprints: compatibility with legacy rolled images” in Proccedings of Biometric Consortium Conference, Baltimore, Aug. 2006, pp. 1-6.

. Jin Qi, Y. Wang. “A robust fingerprint matching method”. Pattern Recognition, vol. 38, pp. 1665-1671, 2005.

. J. Feng. “Combining minutiae descriptors for fingerprint matching”. Pattern Recognition, vol. 41, pp. 342-352, 2008.

. Y. Mei et al. “A gradient-based combined method for the computation of fingerprints’ orientation field”. Image and Vision Computing, vol. 27, pp. 1169-1177, 2009.

. T. Lister et al. “Optical properties of human skin”. Journal of Biomedical Optics, vol. 17, 2012.

. A. N. Bashkatov et al. “Optical properties of skin subcutaneous, and muscle tissues: a review”. Journal of Innovative Optical Health Sciences, vol. 4, pp. 9-38, 2011.

. R. Ohtsuki et al. “Multiple-reflection model of human skin and estimation of pigment concentrations”. Optical Review, vol. 19, pp. 254–263, 2012.

. L. Li, C. So-ling Ng. “Rendering human skin using a multi-layer reflection model”. International Journal of Mathematics, vol. 3, pp. 44-53, 2009.

. S. Biswas et al. “Robust estimation of albedo for illumination-invariant matching and shape recovery”. IEEE Transactions on Pattern Analysis & Machine Intelligence. vol. 31, pp. 884-899, 2008.

. J. T. Barron, J. Malik. “Shape, albedo, and illumination from a single image of an unknown object” in Proceedings of Computer Vision and Pattern Recognition (CVPR), 2012, pp. 334-341.

. S. Suh, M. Lee. “Robust albedo estimation from a facial image with cast shadow under general unknown lighting”. IEEE T. Image Processing, vol. 22, pp. 391-401, 2012.

. D. Maltoni, R. Cappelli. “Advances in fingerprint modeling”. Image and Vision Computing, vol 27, pp. 258-268, 2009.

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Published

2016-02-19

How to Cite

Balogiannis, G., Yova, D., & Politopoulos, K. (2016). A Computer Vision Non-Contact 3D System to Improve Fingerprint Acquisition. International Journal of Computer (IJC), 20(1), 174–198. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/531

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