1. bookVolume 77 (2020): Issue 1 (December 2020)
Journal Details
License
Format
Journal
eISSN
1338-9750
First Published
12 Nov 2012
Publication timeframe
3 times per year
Languages
English
access type Open Access

Openfinger: Towards a Combination of Discriminative Power of Fingerprints and Finger Vein Patterns in Multimodal Biometric System

Published Online: 31 Dec 2020
Volume & Issue: Volume 77 (2020) - Issue 1 (December 2020)
Page range: 109 - 138
Received: 27 Sep 2019
Journal Details
License
Format
Journal
eISSN
1338-9750
First Published
12 Nov 2012
Publication timeframe
3 times per year
Languages
English
Abstract

Multimodal biometric systems are nowadays considered as state of the art subject. Since identity establishment in everyday situations has become very significant and rather difficult, there is a need for reliable means of identification. Multimodal systems establish identity based on more than one biometric trait. Hence one of their most significant advantages is the ability to provide greater recognition accuracy and resistance against the forgery. Many papers have proposed various combinations of biometric traits. However, there is an inferior number of solutions demonstrating the use of fingerprint and finger vein patterns. Our main goal was to contribute to this particular field of biometrics.

In this paper, we propose OpenFinger, an automated solution for identity recognition utilizing fingerprint and finger vein pattern which is robust to finger displacement as well as rotation. Evaluation and experiments were conducted using SDUMLA-HMT multimodal database. Our solution has been implemented using C++ language and is distributed as a collection of Linux shared libraries.

First, fingerprint images are enhanced by means of adaptive filtering where Gabor filter plays the most significant role. On the other hand, finger vein images require the bounding rectangle to be accurately detected in order to focus just on useful biometric pattern. At the extraction stage, Level-2 features are extracted from fingerprints using deep convolutional network using a popular Caffe framework. We employ SIFT and SURF features in case of finger vein patterns. Fingerprint features are matched using closed commercial algorithm developed by Suprema, whereas finger vein features are matched using OpenCV library built-in functions, namely the brute force matcher and the FLANN-based matcher. In case of SIFT features score normalization is conducted by means of double sigmoid, hyperbolic tangens, Z-score and Min-Max functions.

On the side of finger veins, the best result was obtained by a combination of SIFT features, brute force matcher with scores normalized by hyperbolic tangens method. In the end, fusion of both biometric traits is done on a score level basis. Fusion was done by means of sum and mean methods achieving 2.12% EER. Complete evaluation is presented in terms of general indicators such as FAR/FRR and ROC.

Keywords

MSC 2010

[1] AHMAD, M. I.—WOO, W. L.—DLAY, S.: Non-stationary feature fusion of face and palmprint multimodal biometrics, Neurocomputing 177 (2016), 49–61.10.1016/j.neucom.2015.11.003Search in Google Scholar

[2] BARTŮNĚK, J. S.: Fingerprint Image Enhancement, Segmentation and Minutiae Detection. PhD Thesis, Blekinge Tekniska Högskola, Karlskrona, 2016.Search in Google Scholar

[3] BAY, H.—TUYTELAARS, T.—VAN GOOL, L.: SURF: speeded up robust features. In: Computer Vision – ECCV 2006, ECCV 2006. (A. Leonardis, H. Bischof, A. Pinz eds.), Lecture Notes in Computer Science Vol. 3951, Springer-Verlag, Berlin, Heidelberg, 2006, pp. 407–417.Search in Google Scholar

[4] BEN KHALIFA, A.—GAZZAH, S.—ESSOUKRI BEN AMARA, N.: Adaptive score normalization: a novel approach for multimodal biometric systems, World Academy of Science, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering 7 (2013), 205–213.Search in Google Scholar

[5] BRADSKI, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000. http://www.drdobbs.com/open-source/the-opencv-library/184404319, Accessed on: 13. 5. 2019.Search in Google Scholar

[6] CANZIANI, A.—PASZKE, A.—CULURCIELLO, E.: An analysis of deep neural network models for practical applications, Computer Vision and Pattern Recognition, 2017. https://arxiv.org/abs/1605.07678Search in Google Scholar

[7] DAS, R.—PICIUCCO, E.—MAIORANA, E.—CAMPISI, P.: Convolutional neural network for finger-vein-based biometric identification, IEEE Transactions on Information Forensics and Security 4 (2019), 360–373.10.1109/TIFS.2018.2850320Search in Google Scholar

[8] JAIN, A.—FLYNN, P.—ROSS, A. A.: Handbook of Biometrics. 1st edition, Springer-Verlag, 2008.10.1007/978-0-387-71041-9Search in Google Scholar

[9] JAIN, A.—ROSS, A. A.—NANDAKUMAR, K.: Introduction to Biometrics. 1st edition, Springer-Verlag, 2011.10.1007/978-0-387-77326-1_1Search in Google Scholar

[10] JIA, Y.—SHELHAMER, E.—DONAHUE, J.—KARAYEV, S.—LONG, J.—GIRSHICK, R.—GUADARRAMA, S.—DARRELL, T.: Caffe: Convolutional Architecture for Fast Feature Embedding. Technical Report, Berkeley Vision and Learning Center, 2014. https://arxiv.org/pdf/1408.5093.pdf10.1145/2647868.2654889Search in Google Scholar

[11] JIN, L.: Using deep learning for finger-vein based biometric authentication.Towards Data Science, http://web.archive.org/web/20080207010024/,http://www.808multimedia.com/winnt/kernel.htm,52019, Accessed on: 12. 5. 2019.Search in Google Scholar

[12] KAUBA, C.—REISSIG, J.—UHL, A.: Pre-processing cascades and fusion in finger vein recognition.In: International Conference of the Biometrics Special Interest Group (BIOSIG), IEEE, Darmstadt, Germany, 2014.Search in Google Scholar

[13] KÁDEK, L.: Daktyloskopický siětový systém DBOX - server. Master’s Thesis, Slovak Technical University in Bratislava, FEI ÚIM, 2018.Search in Google Scholar

[14] KHELLAT-KIHEL, S.—ABRISHAMBAF, R.—MONTEIRO, J.—BENYETTOU, M.: Multimodal fusion of the finger vein, fingerprint and the finger-knuckle-print using Kernel Fisher analysis, Applied Soft Computing 42 (2016), 439–447.10.1016/j.asoc.2016.02.008Search in Google Scholar

[15] LATHA, L.—THANGASAMY, S.: Efficient approach to normalization of multimodal biometric scores, International Journal of Computer Applications 32 (2011), 57–64.Search in Google Scholar

[16] LOWE, D. G.: Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60 (2004), 91–110.10.1023/B:VISI.0000029664.99615.94Search in Google Scholar

[17] M2SYS:. M2-fuseID smart finger reader,M2SYS, http://www.m2sys.com/wp-content/uploads/pdf/M2-FuseID-web-flyer.pdf. Accessed on: 10. 5. 2019.Search in Google Scholar

[18] MARÁK, P.—HAMBAĹIK, A.: Fingerprint recognition system using artificial neural network as feature extractor: design and performance evaluation, Tatra Mt. Math. Publ. 67 (2016), 117–134.Search in Google Scholar

[19] MIURA, N.—NAKAZAKI, K.—FUJIO, M.—TAKAHASHI, K.: Technology and future prospects for finger vein authentication using visible-light cameras, Latest Digital Solutions and Their Underlying Technologies 67 (2018).Search in Google Scholar

[20] NGUYEN, D.-L.—CAO, K.—JAIN, A. K.: Robust minutiae extractor: integrating deep networks and fingerprint domain knowledge.In: International Conference on Biometrics (ICB), Gold Coast, QLD, Australia, 2018, IEEE. https://arxiv.org/abs/1712.09401Search in Google Scholar

[21] ONG, T. S.—TENG, J. H.—MUTHU, K. S.—TEOH, A. B. J.: Multi-instance finger vein recognition using minutiae matching.In: 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China, 2013. IEEE. pp. 1730–1735.Search in Google Scholar

[22] RADZI, S. A.—KHALIL-HANI, M.—BAKHTERI, R.: Finger-vein biometric identification using convolutional neural network, Turkish Journal of Electrical Engineering & Computer Sciences 24 (2016), 1863–1878.10.3906/elk-1311-43Search in Google Scholar

[23] SHAHEED, K.—LIU, H.—YANG, G.—QURESHI, I.—GOU, J.—YIN, Y.: A systematic review of finger vein recognition techniques, Information 9 (2018).10.3390/info9090213Search in Google Scholar

[24] TANG, Y.—GAO, F.—FENG, J.—LIU, Y.: FingerNet: an unified deep network for finger-print minutiae extraction.In: IEEE International Joint Conference on Biometrics (IJCB), 2017.Search in Google Scholar

[25] TELGAD, R. L.—DESHMUKH, P. D.—SIDDIQUI, A. M.: Combination approach to score level fusion for multimodal biometric system by using face and fingerprint. In: International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), IEEE, Jaipur, India, 2014.Search in Google Scholar

[26] THAI, R.: Fingerprint Image Enhancement and Minutiae Extraction.PhD Thesis,School of Computer Science and Software Engineering, The University of Western Australia, 2003.Search in Google Scholar

[27] TURRONI, F.: Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms. PhD Thesis, Università di Bologna, 2012.Search in Google Scholar

[28] WANG, K.—MA, H.—POPOOLA, O. P.—LIU, J.: Finger vein recognition.In: Biometrics (J. Yang, ed.), Chapter 2, IntechOpen, Rijeka, 2011.Search in Google Scholar

[29] XIE, S. J.—LU, Y.—YOON, S.—YANG, J.—PARK, D. S.: Intensity variation normalization for finger vein recognition using guided filter based singe scale retinex,Sensors 15 (2015), 17089–17105.10.3390/s150717089454192426184226Search in Google Scholar

[30] YALAMANCHILI, P.—ARSHAD, U.—MOHAMMED, Z.—GARIGIPATI, P.— ENTSCHEV, P.—KLOPPENBORG, B.—MALCOLM, J.—MELONAKOS, J.: ArrayFire: A high performance software library for parallel computing with an easy-to-use API, AccelerEyes 106, (2015). https://github.com/arrayfire/arrayfireSearch in Google Scholar

[31] YANG, J.—ZHANG, X.: Feature-level fusion of fingerprint and finger-vein for personal identification, Computer Science, Mathematics Pattern Recognit. Lett. 33 (2012), 623–628.10.1016/j.patrec.2011.11.002Search in Google Scholar

[32] YIN, Y.—LIU, L.—SUN, X.: SDUMLA-HMT: A multimodal biometric database. In: Biometric Recognition. CCBR 2011. (Z. Sun et al. eds.) Lecture Notes in Comput. Sci. Vol. 7098, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 260–268.Search in Google Scholar

[33] ZHU, E.—YIN, J.—ZHANG, G.—HU, C.: A Gabor filter based fingerprint enhancement scheme using average frequency,(IJPRAI) 20 (2006), 417–430.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo