Openfinger: towards a combination of discriminative power of fingerprints and finger vein patterns in multimodal biometric system. (English) Zbl 07439095

Summary: 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.


68T10 Pattern recognition, speech recognition
94A62 Authentication, digital signatures and secret sharing
Full Text: DOI


[1] AHMAD, M. I.—WOO, W. L.—DLAY, S.: Non-stationary feature fusion of face and palmprint multimodal biometrics, Neurocomputing 177 (2016), 49-61.
[2] BARTŮNĚK, J. S.: Fingerprint Image Enhancement, Segmentation and Minutiae Detection. PhD Thesis, Blekinge Tekniska Högskola, Karlskrona, 2016.
[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.
[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.
[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.
[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/1605.07678
[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.
[8] JAIN, A.—FLYNN, P.—ROSS, A. A.: Handbook of Biometrics. 1st edition, Springer-Verlag, 2008.
[9] JAIN, A.—ROSS, A. A.—NANDAKUMAR, K.: Introduction to Biometrics. 1st edition, Springer-Verlag, 2011.
[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.pdf
[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.
[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.
[13] KÁDEK, L.: Daktyloskopický siětový systém DBOX - server. Master’s Thesis, Slovak Technical University in Bratislava, FEI ÚIM, 2018.
[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.
[15] LATHA, L.—THANGASAMY, S.: Efficient approach to normalization of multimodal biometric scores, International Journal of Computer Applications 32 (2011), 57-64.
[16] LOWE, D. G.: Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60 (2004), 91-110.
[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.
[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. · Zbl 1436.94013
[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).
[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/1712.09401
[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.
[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.
[23] SHAHEED, K.—LIU, H.—YANG, G.—QURESHI, I.—GOU, J.—YIN, Y.: A systematic review of finger vein recognition techniques, Information 9 (2018).
[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.
[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.
[26] THAI, R.: Fingerprint Image Enhancement and Minutiae Extraction.PhD Thesis,School of Computer Science and Software Engineering, The University of Western Australia, 2003.
[27] TURRONI, F.: Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms. PhD Thesis, Università di Bologna, 2012.
[28] WANG, K.—MA, H.—POPOOLA, O. P.—LIU, J.: Finger vein recognition.In: Biometrics (J. Yang, ed.), Chapter 2, IntechOpen, Rijeka, 2011.
[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.
[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/arrayfire
[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.
[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.
[33] ZHU, E.—YIN, J.—ZHANG, G.—HU, C.: A Gabor filter based fingerprint enhancement scheme using average frequency,(IJPRAI) 20 (2006), 417-430.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.